These two speculative narratives represent two 13 year olds living 30 years from now and their relationship with education, media, text and technologies.

Dystopia: Makers and Users

Her wrist buzzed again as she got out of the car. The door slid shut behind her and the empty vehicle moved smoothly away down the road away from her morning school. She already knew what the buzz meant and shook her arm briefly to dismiss the reminder. Her parents has set her schedule into her chip four years ago and refused to remove it even though she was a teenager now. If she was at all late they would send her a buzz notification to keep her moving. It was embarrassing and she pulled her sleeve down to hide the slight glow coming from under the skin of her wrist confirming that the reminder had been acknowledged.

She dreaded going to morning school with all of the Users but her few Maker friends who she carpooled with to afternoon school made up for it. Most mornings she could find them in pod 5 curled up on the blue ergonomic couches to catch up with each other before their wrists buzzed for their first class. The crowded public school was unremarkably quiet. Teens relaxed against lockers, shoulder to shoulder in huddled groups, their collective faces illuminated by the blue lights of the screens. They sent messages back and forth, wrists flickering green almost constantly as messages were received and responded to. Keeva had heard that some of the Users has to get their chips moved from one wrist to the other every month to prevent damage from the repetitive motion of accepting and dismissing messages. 

A few groups burst into laughter in unison as one of them shared a particularly funny clip or joke. Their devices were set to synch when they gathered in friend groups and algorithms prioritized and edited messages for clarity. Every group had their own inside jokes, online news and filters to prevent unsafe interactions. One kid, Mark, chucked a granola bar at the back of a girl sitting across the hallway, but Keeva knew that whatever she had sent couldn’t have been that bad or the filters would have picked it up. None of the students spoke but quiet was safer, who knew what would get misunderstood or slip out unexpectedly if you spoke out loud. Devices were set up to filter out things that their users found too troubling or overwhelming, even from their friends. Mark’s device sensitivity filter was set incredibly high, but he still managed to get mildly annoyed on a daily basis. It was better than when they were little though, when he threw chairs and yelled. That was before they were all allowed to get devices full time in grade three.

At 9 am on the dot their classes would be pushed onto their devices and their social synch would switch to classroom synch. They would only be able to connect with their teacher and other kids in their class. The school algorithms determined these groupings to ensure just the right balance of focus and friendship. They didn’t have to be in a specific room to go to class. The teachers had devices that automatically found all of their students on the cameras and they could teach from their offices without ever setting foot in the same room as the kids. This was how she learned every morning and how she would have continued to learn as one of the Users, if that aptitude test in third grade hadn’t sent her to the Maker school every afternoon.

Officially she wasn’t any different than the rest of the kids she went to school with and if the teachers caught them using words like Users or Makers they would get a phone call home and a restorative justice circle before you could say “algorithm”, but they all knew that the differences existed. The fact that they were all in the same school in the morning only emphasized the differences that existed between them and the paths they were being trained to follow. She wasn’t sure what had stood out to the computer on the day-long test they had all taken the day they got their devices in third grade, but the next day she was off to an afternoon Academy that couldn’t have been more different than her regular school. Her parents had brought her out to dinner and called everyone they knew to share the good news that she was now an Academy student, but were surprisingly vague about what it actually meant. As time went on she realized that they really didn’t know. They were from a different generation before the academies existed, before a select group of students were chosen every year to be educated with the goal of creating the algorithms that would enhance the lives of everyone around them. Google, Meta, Micrososft, Uber, they had academies in every town and in this rapidly changing world their approval was all you needed, a guarantee of a good life. What parent wouldn’t love that?

Her parents relied on technology for daily practical things, scheduling, talking to the house, feeding the pets, autonomous cars, but weren’t as immersed in it as her classmates in morning school. People of their age just weren’t. Both her parents used technology regularly for work, her mother used it to diagnose patients and her dad relied on algorithms to decide what to invest in for his venture capital firm, but they still walked the dog themselves and spoke out loud instead of through their devices. They enjoyed the convenience but still insisted on stepping ay from it all from time to time. They read articles on youth these days and their “over-reliance on technology” but she was pretty sure they didn’t know the half of it. After all they didn’t go to school every morning with the Users every morning and the only regular interactions they had with kids were with Keeva and her Maker friends but the rules were different for them.

She and the rest of the Makers had been made to continued to interact much as older generations or very young children did, even after they were given their devices. The Academies had rules about over-reliance on the technology. They were being trained to improve it, not get sucked into it like the Users she went to morning school with. She was going to be responsible for them after all, for creating future technologies that would better adapt them to their everyday lives, keep them them comfortable and safe. She had to be able to see clearly in a way that they couldn’t. Devices were for work, for efficiency, but never for socializing, not for her. Screen time for the Makers was strictly monitored and they were forced to speak out loud when they weren’t in class. It was a pain at first but the habit stuck, so when she arrived at Pod 5 she was unsurprised to be greeted to a chorus of hellos from her Academy friends that would have seemed so rude to the User kids. She barely had time to say hello back before her wrist buzzed again and she pulled out her device to begin her classes. Tomorrow she would get here earlier to hang with her friends before class, she thought as the pre-recorded teacher spoke to her from the screen.

Utopia: A Slightly Better Now

Matthew kicked a ball down the pitch and the ball swished in a satisfying way as it hit the back of the empty net. His wrist vibrated slightly as he went to retrieve the ball and he grabbed his bag before heading towards the school building. The hallways of the school had a few students headed to class, while others chatted with friends. Students were all on individualized programs, so class times were unpredictable and programmed into each students wrist chip and devices to make sure that they didn’t forget their schedule for the day. 

Every student met with a virtual or online guidance counsellor before the start of the year to plan their schedule for the following academic year. Surveys were sent out beforehand to students and parents to clarify academic goals and schedules. Every student needed to go to school, but they didn’t all have to  follow the same yearly or daily schedule, or take all the same courses. 

His dad had been a venture capitalist but got tired of the corporate life. He worked in IT now and insisted on spending half the year traveling and soaking up new destinations. This worked well for Matthew’s mom who, as a doctor, did the vast majority of her appointments online. She worked in a practice where every GP took turns being the virtual doctor for a few months at a time. After Matthew had submitted his academic goals to the school the computer programs took into account his parents wishes and their family scheduling conflicts before producing his yearly schedule of online and in-person classes. 

Until last year he had needed to be present for a September to June school year but as soon as he had turned 12 and was allowed to stay home alone, he was able to participate in hybrid learning as long as he finished his work and kept up his grades. He was a bit behind in English and struggled with his writing so he got extra small group support built into his schedule. Before schools switched to the flexible hybrid schooling there hadn’t been enough teachers to be able to do this but with new algorithms to help with assessment and teachers being able to work remotely for part of the year, it made the profession more appealing. A lot of the hybrid classes had one teacher for a larger group of students, which freed up more time for other teachers to work with smaller groups of students who needed extra help.

He didn’t love reading and writing but he practiced using an online program every day and he was improving. Anything that needed to be read for Science or Social Studies was automatically geared towards his reading level so he as able to keep up in other subject areas. He really enjoyed reading audio books though and had a high comprehension level. 

He and his friends kept up through video calling sometimes when he was away, although he was sometimes a bit disappointed that he couldn’t match up his school schedule to his friends like kids of his age who lived in town all year long. He was able to show them where he was using his VR glasses and sometimes it was almost like having them with him when they were all talking over each other in his headphones as he zip-lined over cloud forests in Costa Rica or brought them along on a Jet Ski in Italy. It wasn’t all new to them and sometimes they said that they felt like they had already been there because of the virtual reality field trips that they took in school. They also liked to watch movies or play video games together some days using VR and synched up video.

He went into his small writing group and flipped open his device. There was a notification saying that the autonomous vehicle would be driving him home at 3.30 and before his class started he adjusted the pickup time on the screen to 4.10 so that he could play some soccer with his friends after school. The device pinged in acknowledgement as his teacher joined him and the three other students so that the class could begin.

The detain and release algorithm program inspired more questions than it answered on the whole. It gave extremely helpful but limited information that made me want to know more about the circumstances of each case. Any time the bars were yellow or red, indicating a high likelihood of a failure to appear, committing a crime or violence, I had more questions than answers that would help me make such an important judgement about somebody’s life. The prosecutors suggestion was often the first thing that I looked at, followed by the likelihood of problems occurring if they were released. I carefully examined the defendants statements at first, but as I progressed with my choices I started looking at them less and less if it wasn’t a grey-area case. Part of what prompted this fairly callous procedure for me were the bars indicating how full the jails were and the fear score for the community. It ended up feeling like points in a video game seeing either of those bars go up and made every subsequent decision more difficult, as I tried to maintain a sense of fairness and empathy while keeping one eye on my “scores”. This system definitely made me more efficient than I would have been having to go through case files, but it removed some necessary human empathy and consideration from the process. 

Algorithms seem like an easy way to streamline processes that can be overwhelming or have large potential for human error. I thought that what O’Neil (2016) said about how “[Algorithms] show up when there’s a really difficult conversation that people want to avoid” was very astute. When we can make something quantitative that’s should be qualitative in nature, it can quickly shut down opposition by elevating the status of processes or decisions with their mathematical associations. Sometimes there are no right answers to problems or situations in the moment, there is only human judgement, which is fallible, but sometimes a better option than many of the processes she described as currently being the purview of algorithms, such as personality tests for hiring and using test scores to assess teacher performance. 

Algorithms can be positive, and I would argue that algorithms are needed as an adaptation to keep up with the expected pace of the world today, at work, school or in our home lives. Despite the benefits offered by algorithms in streamlining our day to day lives, I do have concerns with the direction some of these are taking in order to build more effective schools. When I worked in an English secondary school I had concerns about how student learning data was interpreted by algorithms, in a way that is similar to what was described by Kathy O’Neill. The school that I worked in was given a “four” in all evaluation categories as determined by OFSTED (Office for Standards in Education). A “four” was given the descriptor of “inadequate” and the school was promptly put into a state called “special measures”, which leads to increased scrutiny, benchmark goals to be achieved and more frequent inspections by OFSTED inspectors. This school happened to be in an area where half of the student body was currently on, or had been on benefits (welfare) in the past five years. Many of the challenges associated with educating students came from unmet social-emotional needs, trauma and generational poverty, none of which were addressed by these inspections.

Schools in England have computer systems that evaluate students’ past performance, based on data that has been put in the system and generates predictions of student success the following year. Their system uses levels of progress to evaluate success rather than grades and teachers were told to put large stickers on the front of every students’ workbook with their current level of progress and their expected level of progress before the end of the year. The difficulty with this system was that the criteria for determining the predicted levels was not shared with staff members or students and the predicted levels were very publicly displayed. This led students to wonder why someone with the same current level as them would have an entirely different predicted level. It often left students feeling that they were inherently less smart or less likely to be successful than the student sitting next to them, despite showing similar current skill levels. 

The algorithm that determined the scores was veiled in secrecy and to make matters more confusing, administrators would sometimes input different scores than those submitted by teachers to give the impression that they were meeting goals set out by OFSTED, which skewed the data further. Students who had very low levels of predicted progress would sometimes meet their predicted score before the end of the year and lose all motivation to keep learning because they had already met the expected level that had been laid out for them by the data, leading to somewhat of a self-fulfilling prophecy. Ultimately, I found the lack of transparency about the criteria that the algorithm based these predictions on and the public nature of the data to be overwhelmingly harmful to the students in my class. These programs were also applied to schools across the country in the same way, regardless of a students personal circumstances and didn’t take into account the complexities of my students lives outside of school. I wonder what biases exist within the program that might further disadvantaged students in this low-income neighbourhood.

The question of how to speed up student progress is complex, and while we do not use this same system in Canada we do try to motivate or evaluate students using a number of other algorithms, even if they are less formally applied. Games like Prodigy or No Red Ink have become increasingly popular for helping students gain skills in Math and Language Arts, while providing data to teachers on student progress. While entertaining for students, they can also provide parents and teachers with data about where students are in relation to grade-level. I have often wondered exactly how they determine this, as the process for how the child in compared to grade-level is not clearly laid out. It would be easy as a teacher to base assessment of student learning on these algorithms, especially given class sizes and marking load but I feel that it would do a disservice to students to put too much weight on these assessments given the veiled nature of the criteria for success.

However, algorithms have a place in education given that the people creating them are transparent about how they function. The place where I can see them impacting student learning in the most in positive way is in differentiating learning. By having students working in stations, with some groups working on computer programs tailoring material to their skill level, teachers can better support the entire class and create a more individualized learning environment. There is tremendous potential for AI in education but teachers need to be given the chance to work with those in the tech sector in developing these programs to avoid some of the pitfalls in current educational AI. Much like the detain and release algorithm, educational AI tends to devalue the empathy and consideration of the student as a whole that must be a part of education in order for it to be effective.

References:

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy (First edition). New York: Crown.

Although the Palladio visualization of the multiplex network created by the data gathered from the Golden Record Curation Assignment is unable to communicate the reason behind the choices that other students made, it leaves plenty of opportunity for speculation. Initially when presented with the data in this format I was at a loss as to what I was meant to be looking for and where to start. As I played with the settings and explored it further, I found myself naturally drawn to verifying if others had the same impulses I did in terms of which songs they kept and which they let go. I was particularly interested in looking at groupings of songs to see how many songs of a similar type, or from the same region people chose to include. When the data showed that others had made similar choices to my own it was easy to assume that they had done it for the same reason, but this would have been drawing conclusions based on pure speculation without supplementary data to back up what their intentions may have been. I visited several of my classmates webpages to try and ascertain why they made certain choices on the previous assignment and while some proved to be useful and went some way towards explaining the visualization of the data, most did not directly address the specific data points I was looking at.

Below is an image of the visualization showing the three tracks that originated in North America included on the Golden Record and the students who chose to include these on the record.

The first thing I noticed was that, with one exception, if a student chose to included “Melancholy Blues” or “Johnny B. Goode” in their list they also chose to include “Night Chant”. I would assume that this is because they did not feel that music in North America could be accurate represented without including an example of indigenous music. There were a number of students, however who only chose “Night Chant” and did not seem to think that other forms of North American music were necessary in representing that part of the world. It was also interesting to me that every person who chose to include “Melancholy Blues” also included “Johnny B. Goode”, although people who chose “Johnny B. Goode” did not all feel the need to include “Melancholy Blues”. This indicated to me that more people felt that “Johnny B. Goode” could act as a stand alone representation of North American music in a way that “Melancholy Blues” could not. The chart below shows the visualization that led to these assumptions about people’s intentions. 

I am drawing assumptions based on data that may or may not represent the reasoning behind how every student made their song choices. Even though this has no guarantee of accuracy researchers, pollsters and others looking to interpret data for a number of reasons need to be able to draw conclusions about general intentions using data that does not include further interviews as a part of its collection. Although a visualization such as the one above would likely be much more confusing to interpret with more data points, having more participants would actually have been more revealing in terms of highlighting trends in people’s choices and would have allowed me to draw more conclusions as to possible reasons for these decisions. 

I believe that null choices are remarkably well reflected by the visualization of the data if you put certain parameters in place and carefully choose what data you are looking at and for what purpose. When looking at the visualization, seeing edges coming off of nodes representing songs that lead directly back to nodes with a student’s name and not to the other track nodes makes a powerful statement about what those individuals prioritized. For example, in the first image of the visualization that I included in my reflection there are a number of students who only chose “Night Chant” out of the three songs included in the visualization. This makes a very strong statement about what they placed value on when completing the activity, as they did not choose two much more broadly recognized tracks by internationally recognized artists in favour of an indigenous track. Based on seeing what they did not choose I can draw conclusions as to why they may have prioritized “Night Chant” over a rock and roll or jazz piece. 

In a way, the process of choosing parameters for the multiplex network visualization was not unlike using algorithms on google to narrow down a vast amount on information to only that which would be useful to answer a specific question. The visualization as a whole was far too vast, and even with the limited number of participants, too cluttered to make sense out of it without limiting what was being shown. I could only find exactly what I thought to look for based on the way that I manually restricted the data and it made me wonder how more complicated algorithms or AI might have been able to isolate data that I hadn’t thought to address in this complex web. When looking at a vast number of data points, computers are far more adept than human beings at identifying patterns and highlighting which may be of interest, even while humans are essential for the final interpretation of the data being highlighted.

If data from political surveys is analyzed in a similar way, having data with little context could lead to certain assumptions about why a person may have answered questions in a certain way, despite the fact that there could be many possible reasons for their answers. This could lead to candidates declaring a particular stance but for reasons that their electorate can not necessarily relate to. If voters are grouped in a similar manner using collected data it presents a rather homogenous group that all seem to share the same goals, but the reasons behind those remains largely obscured. Because data is typically interpreted with a specific question in mind, many of the nuances behind why someone may have answered a set of questions in a certain way are obscured in order to focus on what generalizations can be made about the group as a whole.

References:

Code.org. (2017, June 13). The Internet: How Search Works. Retrieved from https://youtu. be/LVV_93mBfSU

The following tracks represent a shortened list of 10 tracks that I felt were most vital to include on the Golden Record:

Track 3: Senegal, percussion, recorded by Charles Duvelle. 2:08

Track 4: Zaire, Pygmy girls’ initiation song, recorded by Colin Turnbull. 0:56

Track 7: “Johnny B. Goode,” written and performed by Chuck Berry. 2:38

Track 11: Mozart, The Magic Flute, Queen of the Night aria, no. 14. Edda Moser, soprano. Bavarian State Opera, Munich, Wolfgang Sawallisch, conductor. 2:55

Track 12: Georgian S.S.R., chorus, “Tchakrulo,” collected by Radio Moscow. 2:18

Track 22: Beethoven, Fifth Symphony, First Movement, the Philharmonia Orchestra, Otto Klemperer, conductor. 7:20

Track 24: Navajo Indians, Night Chant, recorded by Willard Rhodes. 0:57

Track 27: Peru, wedding song, recorded by John Cohen. 0:38

Track 28: China, ch’in, “Flowing Streams,” performed by Kuan P’ing-hu. 7:37

Track 29: India, raga, “Jaat Kahan Ho,” sung by Surshri Kesar Bai Kerkar. 3:30

My first thought when trying to choose 10 songs to represent humanity out of the songs currently on the Golden Record was that some of these songs were very jarring and I didn’t feel like I would have any trouble getting rid of some of them. As with many first impressions this turned out to be wholly incorrect, and some of the songs that had originally fallen on my mental trash heap ended up feeling so essential that they dethroned favourite musical pieces from my final list. As Abby Smith Rumsey said “imagination really is about memory in the future tense” (Brown University, 2017) and ultimately my imagination of what would listen to this record, when or how they would interpret it is entirely based on my own experience and memory. I would hardly know the right way to go about choosing from this list of music for a life form I am familiar with like a dog or a fish, much less an entity that I can’t even conceive of. The idea proposed by Timothy Farris that these songs could be interpreted mathematically was key to making me rethink my approach to this task and move beyond just which songs I found to be the most recognizable or enjoyable. While I don’t have a strong enough grasp of the precise mechanics of music, much less the mathematics that may go into it, I did try to narrow down goals to guide my approach.  

My goals ended up being the following:

1. One song from every inhabited continent
2. Prioritize songs that include human voice
3. Pieces that I find have the most global influence

I wanted the songs to be representative of many cultures, not exclusively my own. If the record is truly to represent humanity in 10 songs it needs to represent a diverse range of musical traditions, even with the understanding that most cultures will still be excluded with a list so short. I thought that this would be the easiest goal to prioritize but I ended up failing to meet it because I had to make a decision between representation and which songs I felt had the most global influence on humanity’s musical traditions.

I chose to prioritize songs that included human voices because I feel that it created an intimacy and connection that purely instrumental pieces do not. Granted, this was a risk as it is entirely possible that whatever creature would be interpreting this record would not have the ability to hear the human vocal range. Most of the vocal tracks are also paired with instruments in the event that this is the case and I felt like it was a risk that I was willing to take because of the impact that the human voice could have on the listener on the off-chance that it is within their hearing range. I originally found the Peruvian Wedding Song to be extremely unpleasant and was more than happy to cut it from the record, but eventually determined that it was essential because of the combination of its continent of origin and vocals. It was the only song on the record that had only one singer that was completely unaccompanied way and instrumentation, which provides a unique and insightful example of the human voice. “Tchakrulo” also made my list because it was the only choral piece on the record, which I felt presented a different dimension to the impact of many voices singing together.

I feel that I can best justify the pieces I kept by discussing a few of the tracks that it was hardest for me to eliminate. “Morning star” and “Devil Bird” was a particularly hard track to remove from the list because this went against my primary goal of having one song from the musical tradition of every continent. Ultimately, I removed it to prioritize the inclusion of both “Jaat Kahan Ho” and “Flowing Streams,” which I felt were both essential in showing incredibly influential musical traditions from the continent of Asia. Although “Morning star” and “Devil Bird” were the only examples of indigenous Australian music, I didn’t feel that it had the same global influence as the other two tracks previously mentioned. “Melancholy blues” was also a painful one to take off the list because of my own admiration for Louis Armstrong and the emotional connection I feel to his music. Ultimately, I ended up removing that track because I felt that some small parts of the jazz tradition he represents were also present in the building blocks of rock and roll. That tradition is well represented by “Johnny B. Goode”, which also had the added benefit of having a vocal element. Removing Bach entirely felt like one of the biggest decisions but I decided that European baroque musical tradition was in some way represented by Mozart as this style served as the foundation for his classical music. I ended up choosing Mozart’s “Queen of the Night” aria because it displayed a vocal experience not found in any of the other tracks, while representing European classical music along with Beethoven’s 5th symphony.

Another consideration in choosing the final 10 was the idea of redundancy. Once I had at least one song to represent each continent it became important to decide what other songs could be added without fulfilling a similar function to a song that was already on the list. I debated about keeping both the Pygmy girls’ initiation song and Senegal, percussion but eventually decided that they represented two vastly different elements of African musical tradition. In this case a purely vocal piece could not be representative of a continent like Africa with its strong and meaningful drumming tradition. Although more recent musical traditions in North America were well represented by ”Johnny B. Goode,” I felt like the “Night Chant” was necessary to provide an example of music that exists on the continent outside of the European and African traditions that came together to create rock and roll.

References:

Brown University. (2017). Abby Smith Rumsey: “Digital Memory: What Can We Afford to Lose?”

In trying to decide on a topic for my Twine, I worried that a longer more creative story with a lot of twists and turns might be too overwhelming for my first time using the software. For my first foray into Twine I created an interactive learning story for a hypothetical dog-sitter trying to navigate the wake up routine for my sweet and elderly dog, Daisy. I realize that this is my second task that has focused on her but she is my constant companion in this online master’s program and a source of everyday magic and inspiration. 

Please follow this google drive link to view my Twine about Daisy’s Morning Routine. If you download the file and click on the file in your downloads folder, it should open up the Twine on Google Chrome.

https://drive.google.com/file/d/1BwlAZ2MyniWt1KPEAqMWPG2Wei00Y4Vo/view?usp=sharinghttp://

The strategy I used in creating this simple hypertext story-game was to create the most complicated and involved option for the story first, which served to create the vast majority of the screens. I continued the process by creating the most straightforward storyline next and determining which slides still needed to be created and how the most complicated storyline needed to intersect with the most straightforward one. The purpose of the story was to simulate troubleshooting strategies for completing part of a morning routine with an unfamiliar dog, and although the participant is meant to feel that they have the ability to change the story along the way, all versions lead to the successful completion of the morning routine by the dog sitter.

The process of trying to formulate links and move the story forward using hypertext links reminded me of a game I used to play during my time as an undergrad. The game involved several competitors using the random page button on wikipedia to access an article and using the links within the text to navigate around Wikipedia to reach the page about toothpaste and be declared the winner. Although the game is undeniably silly, it does highlight how my generation interacts as naturally with hypertext as previous ones did with other literary forms. The chief benefit of hypertext links in benefiting human intellect is the emphasis that it puts on linking ideas to one another and creating an interconnected web of understanding. 

The story that I created was originally intended to be much longer but through the creation of the hypertext game I was naturally drawn towards the minutia of each decision and wanted to follow each option through to its natural conclusion. This text medium helped to clarify options and potential outcomes of decisions, lending itself well to more analytical thinking.

I created the potato prints to spell out the name of my dog, Daisy, and used a paring knife to carve into potatoes from the little potato company. Although the process was fairly straightforward, carving the letters in a way that differentiated between upper and lower case letters proved difficult with such small potatoes, so I opted to make all of the letters upper case. It was also difficult to achieve a consistent size or style, so as to give the impression that the letters belonged to a deliberate font. Furthermore, I had to repeat the carving process of several potatoes due to a badly aimed stroke of the knife, and because I forgot, when it came to the practical application of something I knew theoretically, that the letters would appear in mirror image when printed. I didn’t figure this out until I carved the “S”, as the “D” could be turned so that it could still be used to print even if it wasn’t intentionally carved as a mirror image from the start. I was able to print the duplicate words with fairly consistent spacing and the only notable differences between the two prints are the pressure with which each letter is stamped, which led to varying imperfections in the letters. The entire process from start to finish took me about 10 minutes.

This process was not time saving or an improvement, from and efficiency standpoint, on existing typing methods. However, it offered an artistry of sorts that is missing from typing on a computer. In the video Upside Down, Left To Right: A Letterpress Film by Danny Cooke (2012), the speaker talks about the care that is put into each traditionally typed piece and that he feels that people can recognize the quality of it and value his traditionally produced printed works. Although, I am fairly certain that no one will value my rainbow “Daisy” in quite the same way that they value his products, I developed a certain attachment to both my potato stamps (they are still in my fridge) and the product itself, in a way that I wouldn’t have using methods more commonplace methods. There is value in more traditional processes, ones that require more labor and greater connection between the creator of the text and the product itself. 

Carving five letters was time consuming and I can only imagine the labour that it took to carve entire pages from wood or other materials as they would have done before the invention of movable typeface. Although I am very much in favour of the mechanization of printing, using typeface that is not made out of produce, and all of the more advanced technological methods that exist today, I believe that all of these advances in printing have to take into account what is lost in increasing efficiency. Gutenberg, as noted in the readings, was not necessarily trying to change the product of hand-written manuscripts being produced at the time with his movable typeface but was trying to make the process “more economical”. With new printing and reading technologies we still try to keep practical advancements from taking away from the connection that the reader has with the physical book itself. Many readers, for example are advertised as having screens that look and feel similar to pages of a book rather than the screen of a tablet. Mechanization of writing was necessary for a highly literate society but this doesn’t mean that handwriting  or early printing practices need to be completely erased as new technology emerges.

References:

Cooke, Danny. [Danny Cooke Freelance Filmmaker]. (2012, January 26). Upside Down, Left To Right: A Letterpress Film [Video]. YouTube.

Below is my unedited voice-to-text story for your perusal. Please see my thoughts on this text below this transcript.

“so this one time when I was in Paris I got off the airplane and I was at the airport and was trying to get to downtown Paris but wind up happening is my two bags of me have a social the bag that I had in front of me and then I also had a really suit case studies on the train platform is extremely careful because you know that sometimes are sketchy people waiting on the train platform and wanted up in any trouble Lord you know having to deal with anyone being rude or trying to grab my stuff sizing pretty carefully keeping a lookout but I was also waiting for a train and I was bored so I started playing on my phone I don’t have a flight angry birds are kiddie crush or whatever but I wasn’t really paying a lot of attention to the time I crinkled up and it’ll divide behind me in the opposite problem I thought it was supposed to be out but it was clearly the right train it was an express train to Paris so I quickly shove my phone into this little side purse that I had kind of in front of me is little cross body bag and I started working for the train before I can get very far this woman ask only you know block my way I should these two huge tote bags and she started doing you know we starting I will dance they do trying to get around each other we were very get it at you I was going one way she went the other you know but then we started kind of wishing you’re back and she ended up in front of me again and you know just thinking IN such a class students as a school dance banana just whatever a little bit too long and I started thinking like this is bizarre just seem to want to get past me what is going on but eventually to get around this woman and he started hitting my train you really little really die this bad sense about that there was something that was happening that wasn’t quite right so right before supply chain I looked down into my little black side purse that I had you known for me and I thought of my won’t was missing it was this little pouch that didn’t even hardly look like wallets but it had all of my credit cards at had all of my idea except for my passwords if I didn’t have it I mean I made up a card and had my scuba diving card and I absolutely everything in it that was super super important for me to be able to keep traveling I think now looking a little bit guilty but maybe I’m wrong so I look them in the eyes and I start trying to remember in my French which is usually pretty good but when I’m  flustered a gets a little bit worse so I decided to dip into my absolute Lee most annoying teacher French and ended up telling her that frankly taking my wallet wasn’t very nice but she was very confused but nevertheless I persisted and continue tell her that she need to get back my wallet asking did look confused as she made a couple of sounds but I don’t actually she spoke French so I was thinking it was actually barking up the wrong tree and then maybe I lost my wallet somewhere else when she finally decided that it might be in her best interest to give me what I wanted so she had one hand that was propped up on her totebag and decided that I would rather make my dreams and tried to engage in any more conversation with this thief associate in him me back my wallet apologized for taking at her she sees that it was an accident actually I was little confusing to me as to how you pick pocket somebody’s wallet by accident but I really do think that she thought I was going to get on the train before I notice my wallet missing into would have to deal with me unfortunately for her that nap and I did reported to the security guard the next train station but unfortunately the very front porch the entire thing and said that it was Paris and well what do I want what I expected to do about the pockets I figured the conversation something that they weren’t really interested I think this is the least of their worries back pocket”

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The text above, which I recorded on Notes using the dictation option provided by my MacBook Pro, deviates startlingly from the conventions of written English in about every possible way at some point in the text. Punctuation is non-existent, words have been misunderstood and transcribed as other words, or as a series of other words that do not come together to produce meaning. The piece itself doesn’t function as a written text as it is nearly impossible to follow the meaning of it, even when reading it as the person who originally dictated it.

Generally the program was able to identify proper nouns and correctly capitalized them. Some portions of the text were remarkably well transcribed and would have provided the reader with a fairly accurate sense of the progression of the story had these not been followed up by so many illegible sections. Furthermore the software did seem startlingly good at transcribing idioms like “barking up the wring tree” and “the least of their worries”, which tells me that it was specifically trained in transcribing these. Overall the dictation software seemed to thrive on predictable grammar and sentence structure, which spoken language does not always provide in the same way as the written word. Had I written the story out first and made use of a process similar to the “‘secondary orality’ of present-day high-technology culture, in which a new orality is sustained by telephone, radio, television, and other electronic devices that depend for their existence and functioning on writing and print” (Ong, 2002), my final transcript may have born a closer semblance to the original story. As Gnanadesikan (2011) stated, “written pieces are more carefully crafted than a typical spoken sentence.”

The most grievous errors that interfere with meaning seem to stem from the dictation software misunderstanding what was spoken and erroneously transcribing the word. This led to the word “wallet”, which was central to the story, being transcribed as “won’t”. The purpose of writing is to clarify and refine meaning, which is why when non-standard language is transcribed in this dictation I consider it an error.The software also transcribed sounds without taking into account the entire context of the sentence. This leads to mistakes like “absolute Lee” instead of “absolutely”. In this case, the software heard what I had said very accurately but was not able to take into account context and ended up transcribing it into two different words by focusing on the sounds of the syllables and reorganizing them in a way that made perfect sense if the meaning of the text were no object. 

This reminded me somewhat of King Ibrahim Njoya, ruler of the Bantum people of Cameroon, who in 1896 had his language converted into written form by codifying it into 73 syllabograms and 10 numerals (Gnanadesikan, 2011). If written English were traditionally transcribed syllabically, the dictation software would have encountered far fewer errors. Although, reading a text in this way with a dictation software that presumably would not be able to identify how syllabic symbols should be grouped into words, would provide the reader with a more challenging reading experience as they would have to group the syllables themselves into a meaningful text. 

The story that I dictated is not one that I have ever written down, although I have retold it to friends countless times, because how often does a person actually get their wallet back from a pickpocket? I realized after reading the dictation that, even beyond the glaring written errors, the story loses much of its meaning and relevance by being transcribed in the way that I usually speak it aloud. My oral stories rely heavily on gesture and facial expressions to land the punch lines and draw the listener in; a written transcript just doesn’t provide the audience with the same intended experience as hearing it told aloud. Even if the piece were perfectly transcribed the experience of reading it would be similar reading a play instead of watching it be performed. The story, although it has been written down is a monologue, rather than a written short story.

Ong focuses on how “oral cultures” operate in a way that is different from literate cultures. I find that even literate cultures who rely heavily on their writing systems, like our own, have an exclusively oral tradition for some parts of our human experience. Everyone has stories that they tell and retell, changing the structure of the telling each time to improve the effect on the listener. For many of these stories there is no intention that they will ever be written down and the entire process of refining the piece lives exclusively in the practice of an oral tradition within a literate society. The refining process of turning an oral story into a written one is substantially different, with practiced gestures being replaced by adjectives and adverbs, vocal emphasis by punctuation. I would argue that a strong oral tradition continues to exist in literate societies, although we no longer depend on it for organizational systems like laws or trade. Its primary purpose in our society is to build social connections between people and aid in developing a shared understanding using the patchwork of small occurrences that make up our day-to-day lives.

References:

Gnanadesikan, A. E. (2011).“The First IT Revolution.” In The writing revolution: Cunieform to the internet. (Vol. 25). John Wiley & Sons (pp. 1-10).

Ong, Walter, J. Taylor & Francis eBooks – CRKN, & CRKN MiL Collection. (2002). Orality and literacy: The technologizing of the word. New York; London: Routledge.

My 30th birthday at the archaeological site of Çatalhöyük in Turkey in 2019. Will my bag still be around 8000 years from now?

The contents of my versatile and multi-purpose travel bag.

Even choosing a bag for this activity was a true archeological dig. There are only two bags that I keep consistently packed at all times, my dog’s travel bag and my carry on travel bag. Although analyzing the contents of my dog’s bag may have proven to be in interesting exercise I felt that my own, sadly still unpacked travel bag, might say more about me. The bag is not packed at all times on purpose, but rather remains full because I seldom get around to cleaning it out between trips. It acts as a convenient and constant receptacle for the items that shift through the pockets and transient handbags of my days and weeks. I stopped using a purse last year when I finally decide that I was tired of forgetting it everywhere and opted to find coats with big enough pockets to carry the essentials. These essentials, and items that would have at one time been rolling around in the bottom of a massive handbag, seldom used and often forgotten, are mostly stored in my travel bag. It sits under desks, on chairs and at the bottom of my closet, being shifted around my apartment until it is needed for my next trip or until I need to swap out one essential item for another. One day I might need my watercolour set, another my headphones. I tend to leave these items lying around the house after they have been used, until I can’t reliably locate them and I gather them all to return them to my travel bag in one frustrated sweep of the apartment. Right now I am using a blue Fjallraven backpack for this catch-all bag with a UBC education logo emblazoned on the front that I received as a gift for hosting a teacher candidate last year.  

I don’t have a daily need for all of these items and some are more aspirational than strictly practical, so I will focus on the following ten items:

1. Sunglasses: I got laser eye surgery to correct my vision about 9 years ago and my eyes have been more sensitive to the sun ever since. The sunglasses are as essential to me during the summer as they are forgettable. The cord attached to them keeps me from losing them and the headache medication is what I use when I do forget my sunglasses. 
2. Sunscreen: I love outdoor activities, especially ones that take place on the water when the sun is shinning. I also like to travel during the summer to sun soaked destinations when COVID restrictions allow. Genetically speaking however, I am more suited to life in a dark cave during the summer months, so I slather on as much SPF 110 as I can, cover up, and hope for the best. 
3. Watercolour Travel/ Art Kits: During March 2020 I was home bored and looking for another hobby to add to my extensive list of existing interests. I had always wanted to be proficient at sketching and painting so I bought a tiny watercolour travel kit and gave it a try. I started with online IGTV tutorials and eventually started painting landscapes from my travels. I’m not amazing yet, but it is something I enjoy and that I can see myself improving in slowly but surely.
4. My Phone: I am about as attached to my phone, represented here by my glittery case, as most people of my generation. I primarily use it for browsing instagram, reading emails and playing word puzzle games, keeping in touch with friends and family and navigating through life using Apple Maps.
5. “The Once and Future Witches” by Alix E. Harrow: I have always been an avid reader of fantasy and briefly tried out TikTok last year, where this book was recommended. 
6. External Battery Packs: I sometimes forget to charge my phone and always choose the cheapest flights when I travel, resulting in ludicrously long layovers. Multiple battery packs keep my digital life running. 
7. My Red and Blue Wallets: My red wallet is for the daily essentials, while the blue one is for travel and when I want to use gift cards. The red one is practical and populated by credit cards, debit cards and IDs. The blue one however, is more interesting and would probably tell someone far more about me. You would find my favourite stores in the form of gift cards, my advanced open water scuba certificate and a boat license that everyone should hope I never use, since I have no practical skills that would allow me to actually pilot a boat.
8. My Passports: I have dual citizenship with Canada and the USA and travel with both passports.
9. My Pile of Receipts: I have never and will never categorize these, use them for budgeting or submit them for reimbursement. The receipts at the bottom of my bag do nothing but provide unnecessary cushioning for my other items and remind me of silly purchases like the assortment of airport snacks listed in the crumpled one pictured.
10. Coin Pile: This pile used to be more interesting pre-March 2020. Now, it is mainly an even mixture of US and Canadian coins. The assorted coin pile is usually a pretty accurate way of counting the countries that I have been to that year. 

Although only some of these items could be considered to be traditional texts, I can be read through all of them. Someone could tell my skills level at art and when I likely started through looking at the half empty pans in my pallet, my watercolour landscapes, the fact that I use too much black when I should be mixing contrasting colours to create a more natural looking dark hue. Through items with a small amount of digitally printed text like the gift cards, bottles, coins and passport stamps they could tell far more. They could tell the place of my birth (USA) the country that I immigrated to and became a citizen of (Canada). They could tell my ethnicity through the SPF on my sunscreen and my favourite stores through the logos and text on the gift cards. They would see that I am a scribbler, a doodler by the pens and scraps of handwritten text on the back of the receipts. 

If they looked into my phone or my book they could find out even more about me. They would see that I have audiobooks in French and guess that I am bilingual. They could assume from the subject matter of the book that I chose that I like fantasy, history, feminism and a good dose of magic. They would see that I have audiobooks on my phone and that I like to listen to language, but that I don’t have any ebooks, preferring to feel the paper in my hands. They would be able to see my love of animals through the overwhelming number of photos of them on my camera roll.

This bag 15 years ago would have been less organized but would likely have included many of the same elements. I know myself fairly well, my strengths and shortcomings and feel comfortable that the things I choose to carry with me reflect who I see myself as. I try to be honest with the world. Most people who know me, even in passing, know that they will likely have to chase me down to return my phone to me and that I have more hobbies than I can possibly discuss without boring people, not that this always stops me. I am surprised by how much this bag says about me, although I suppose I shouldn’t be considering that I curated it for travel, allowing it to become a microcosm of my greater life as I hit the road for a new adventure.