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CrosspostKnitting a Scarf to Visualize the Questions Asked in the Makerspace (Crosspost from TRU Library Makerspace Blog)

Makerspaces, Posts

Note: this post is cross-posted to the TRU Library Makerspace Blog and the original was posted here: Knitting a Scarf to Visualize the Questions Asked in the Makerspace – TRU Library Makerspace (C7elelkstén’)

We recently added a king size Addi circular knitting machine to the Makerspace, and inspired by Making Research Tactile: Critical Making and Data Physicalization in Digital Humanities I decided to make a scarf that would visualize the number of questions we’ve answered in the Makerspace since we opened in March, 2022.

Our Addi kindsize knitting machine

Our new Addi kindsize knitting machine

The machine lets you quickly knit either circular tubes or flat panels, and I found it surprisingly easy to use as someone who doesn’t know anything about knitting. The main thing I learned is you need to be careful about how much tension you put on the yarn as it goes into the machine, and you also need to watch carefully at each end or you risk dropping stitches. I dropped a few and had to fix them later, which I will show in a minute.

Whenever we help someone in Makerspace we make a tick on a piece of paper under the appropriate technology. These are called “Reference Interaction” and each tick is one interaction, not one question: if you ask 3 questions all at once we record that as one tick, and if you later ask another question we record that as another tick. This is all entered into a system that records the library’s stats. We have had over 10,000 of these interactions in the Makerspace since we opened!

The spreadsheet I used to calculate the number of rows I would need to represent the data

To turn this data into a scarf, I did a little experimental knitting until I figured out it took about 8 rows to make an inch. Since I knew I wanted something around 30 inches long, I played with the data until I figured out that if each row represented 300 interactions, I would get a scarf that was 33 inches long. I set up a spreadsheet to calculate this and added a colour, order, and “knit to” column which would tell me when to stop and change yarns. I then riffled through our new yarns in our community fabric stash until I found enough contrasting colours made of low-quality material to make the project.

The knitting machine in action!

Then I started knitting! I used the row counter until I got to the right number, then cut the yarn and tied it to the next colour. This is another place where it’s easy to make a mistake, and a couple of times the yarn came untied or risked getting stuck in the machine. It pays here to go slow and learn how to tie a tight knot. In total, it required 269 rows and I estimate it took about 2 hours in total to make.

As I said, I dropped a few stitches so I googled around a bit and found that many people just take a new piece of yarn, loop through the dropped stitch and weave the ends back through the finished piece to hold it in place. I did that, badly, using a contrasting colour that would show my work. My theory is it’s always good to show when you make mistakes, especially when you run a Makerspace and ask people to try new things. Luckily, I make plenty of mistakes.

The fix for the drop stitches

And here is the finished project! It will be hanging in the Makerspace until it gets cold enough one of us takes it down to wear it, so check it out next time you are here. I’d love to see people using the new knitting machine for all sorts of projects, as well as people experimenting with data physicalization and other kinds of data visualization. If you have questions or ideas, let us know or come by one of our co-working sessions.

The finished scarf

Adrian Tchaikovsky’s “The Expert System’s Champion”and LLMs as Perspective Simulacra Engines and/or Frankensteinian Sacks of Neural Meat

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Like many, I’ve been playing with LLMs over the past two years, and my own understanding of these technologies continues to evolve the more I use them. There are obviously many problems with these technologies and how so-called AI is being developed, implemented, and used, but it’s also been a long time since I’ve been as fascinated by a technology or as impacted by its use. They are deeply weird and unpredictable, both powerful and macabre.

LLMs as Perspective Simulacra Engines

One way I’ve been thinking about LLMs is as engines for simulating different perspectives. LLMs are mathematical models trained on huge amounts of content, much of it writing from different people’s perspectives. This data is represented as connections with different weights. People, especially at first, tend to use very generic prompts, so what they get back is influenced by the “heaviest” weights, which tend to be pretty average/mid/beige. As you learn to use more specific prompts and give it more context, you trigger “lighter” or “deeper” connections, which results in more unique, weird, and useful output.

Don’t take any of this as a technically accurate account of how LLMs work.

What I’m often doing is giving the LLM some original content (an idea, proposal, etc.) and trying to get it to simulate a specific perspective through both the prompt and the content. My partner says this is a very librarian way of thinking about LLMs: I’m kind of using them as a “library” of simulated perspectives that I can “retrieve” and apply to my work (superficially this is like a library, but it’s also clearly not at all like a library for there is nothing to collect, retrieve, or curate). For example, I’ve asked LLMs to ask me questions about a program I’m developing from the perspective of future attendees, and to review an idea from the perspective of someone passionate about our pedagogies or values. Prompts like this that are given along with well-formed original documents can be extremely powerful ways of rapidly testing and revising ideas.

There’s another way of seeing this metaphor.

LLMs as Frankensteinian Meat Sacks of Collected Neural Tissue

I recently finished “The Expert System’s Champion,” the second in a series by science fiction author Adrian Tchaikovsky. Tchaikovsky is very good at exploring what non-human sentience might be like, and his “Children of Time” series is particularly good in this respect. This book felt more mythic than his others, reminding me a little of Becky Chambers’ Robot and Monk series or Ursula Le Guin’s Earthsea series.

I won’t try to explain the whole plot, but the next two paragraphs do contain spoilers.

The book is set on a planet where colonization mostly failed due to a hostile biology. Generations later, what remains is something like medieval villages organized around engineered “hives” of “wasps” that pick individuals to fill roles like law-giver and doctor, burrowing into them, and turning them into “expert systems” that more-or-less take the person over to fulfill the role needed to manage the community. These wasps were one of the ways the original colonists managed to survive on the planet. All this has been forgotten and the people who live in the villages who have their own myths and understandings of these systems and the world they live in.

In this second book, the colonists encounter a species of giant snails with another sort of hive structure organized around a truly giant queen snail. This species survives by being capable of hybridizing with anything it encounters, integrating whatever it consumes into itself and the hive, including memories and, for lack of a better phrase, the ways of being of the thing it consumed. There was a splinter group of colonists who were consumed by these snails shortly after they arrived.

“There is nothing of us but Leviathan and her dreams,” uttered the thing called Geordi. “I told you. Before she took us, I told you. Whatever she swallows becomes her mind. Whatever she takes within herself becomes a dream in her, and though she is slumberous and insensate, those dreams can think for her and advise her and give her a mind and a purpose. And so she calls to the Children, our Children, in the voice we give her, and they act as her hands and haul her across the land. You stand within her, and so you will be her dreams soon enough, and perhaps you will guide her purpose when she dreams you.”

Adrian Tchaikovsky in “The Expert System’s Champion”

In this quote, we meet one of these colonists, entombed in the wall of the queen, “explaining” what it is like to be entombed in the queen and part of her mind. The scare quotes are because it’s clear the consumed colonists don’t live anymore, but rather have become part of the snails. It’s not at all clear if the snails themselves are “conscious” beyond the abilities they have from those they consumed.

This is another way of seeing LLMs: as giant meat sacks of neural tissues, harvested by a marauding evil doctor, spliced together and reanimated by the electrical sparks of your prompts.

I wish I could say all this was leading somewhere deeper than wanting to write that last paragraph.

3D Printed Tokens hanging on the glass wall outside the Print Room

Three Challenges for Makerspace Users

Makerspaces

I give a talk when classes and other groups visit the Makerspace that is meant to explain what we are, how we can be used, and hopefully makes them feel welcome. That’s a lot to accomplish and in the first year or so I was trying to fit in too many details, which made the talk overwhelming, even to me. The important parts were often drowned out by all the details.

This year, I took advice from Design Is Storytelling by Ellen Luptin and The Art of Gathering by Priya Parker and redesigned the talk to be shorter, punchier, and focused on values and calls to action. One thing I added is that I now end with 3 challenges. I’ll share them here along with some comments about how they connect with the values I ‘m trying to promote in the space.

Make something personally meaningful to you

“Making as learning works best when you have a personally meaningful project. When a project means something to you, you will work harder to make your vision a reality — your vision will be here (usually I show right hand wayyy to the right) and your current abilities will be here (show left hand wayyy to the left) and you will do all the things you need to do to close the gap between them. This might mean starting over a couple of times, learning new skills, and finding people to help you. What you do in Makerspace doesn’t need to be connected to your classes, so think about how you might use the Makerspace for something you really care about.”

In my experience, personally meaningful projects really do result more learning, better stories, higher-order learning outcomes, and more creative objects. That is why we don’t require users to only use the space for class assignments. It’s important to give users explicit permission to make something they care about because they often think they can only use the space for class assignments. This is especially true for equity-seeking groups who don’t see themselves as allowed to use the space.

This is also why I require faculty who want to use Makerspace for assignments to give students choice about what modality/tool they use and as much freedom as possible for what they are making. Makerspace assignments that require everyone do the same thing (e.g. 3D print a particular kind of object) aren’t really makerspace assignments, they are lab assignments that happen in the Makerspace.

Make a story illustrated by an object

“Sometimes we get too focused on the things we are making and lose sight of why we are making it. This can cause us to make the thing we can already make, instead of what we actually want to make. Instead, try thinking about what you’re doing as telling a story that is illustrated by the thing you’re making. Why are you making this thing? Who is it for? What will it mean to you and others? What impact, good and bad, might it have? Who helped you make it? Think of the story as a container for the thing you’re making that explains what it is meant to be and why it is important.”

I’ve written before about stories being the proper way to talk about outcomes in makerspaces and this challenge is really about starting with that idea by getting people to use stories as framing devices for their projects. Users often become hyper-fixated on what is possible instead of thinking about what they really want to make. Being practical has its place, but often I want them to think bigger instead of thinking practically. Stories help them do that by giving them an outcome that is achievable even if the thing they want to make is going to require a lot more work, resources, collaborators, time, etc.

Stories also foreground what is meaningful about what they are doing, and how it fits into a social/ecological context of place, community, nature, class, etc. It makes them think about the people who will use the things, and the people who helped them make the things.

Make a gift for someone

“The third challenge links the first two: Consider making the first real thing you do in Makerspace a gift. Gifts make things meaningful and ties them to stories about people and community. It helps us think about who the thing is for, how it will be used, and if it will have a positive impact. Consider making a gift for a friend, a family member, or a community organization. The identity token you 3D print in Makerspace is a gift for the space: a piece of communal art that shows the impact of our community.”

This challenge has a number of purposes. Giving the first thing you make after learning a new skill is an important part of Indigenous teaching, and this challenge is meant to bring that learning and spirit into Makerspace. It is also the last thing I say in the welcome talk, and so it is a bookend to the land acknowledgement that I use to open to the talk, which includes a call to be good stewards of the land, resources, and community. In this way it is meant to subtly help users see that they are anchored in a community and that they have a responsibility/role to make it a good community that has a positive impact.

Hooks not Directives

I think of these challenges as hooks I’m establishing that users might later use when thinking about what they are doing, and why they are doing it. They aren’t meant to be rules or directives, they are meant to subtly shift users towards thinking more ambitiously and about context and community.

Storytelling should be the primary frame for (more than just) makerspace outcomes

Makerspaces, Posts

It matters what thoughts think thoughts. It matters what knowledges know knowledges. It matters what relations relate relations. It matters what worlds world worlds. It matters what stories tell stories

Staying with the Trouble: Making Kin in the Chthulucene – Donna haraway – 2016, p. 35

Three interrelated draft-y thoughts about stories in Makerspaces.

Stories should be the primary frame for outcomes

The idea that makerspaces are spaces where inventions and businesses are created is greatly exaggerated. A friend of mine who travelled to makerspaces around the world told me that one thing he learned was that nothing ever gets finished in a makerspace. Instead, they were spaces where people met teachers and collaborators, gained access to technologies, and learned how to learn. If their goal was as concrete as a patent, that happened later in purpose-build spaces. This isn’t a weakness, the two types of spaces serve different purposes and are not really compatible.

Especially in post-secondary institutions the value of makerspaces lies in a meta-skills users develop: a strong sense of self-efficacy with design and technology, skills for life-long learning, and access to a community and space that provides support and a sense of belonging within the institution.

Stories capture the rich activities of what happens in the makerspace better than statistics or even the things made. Most of the actual things made in the makerspace are not that interesting in themselves. They might be interesting to the user, but as object they are often simple, or stereotypical. Stories let us expand out from those objects to talk about what we actually value.

Storytelling should be the goal of most makerspace projects

Something I see a lot is users limiting themselves to things that are achievable in a short amount of time and with their current skill set. This results in stereotypical off-the-shelf projects that don’t make them go beyond their existing knowledge or imagine alternative solutions to problems.

Rather than focus on specific outcomes (an object, a video) we should encourage users to think about what they are doing as telling a story that is accompanied by an object. This expands the range of possibilities users can explore and explicitly values the process of learning over the specific objects. It allows users to tell a more ambitious or speculative story because it’s okay if the object they create to go along with it is non-functional.

Stories don’t need to be finished, functional, or real. A story can be stretch. Often users are hyper-practical but when asking big questions (climate change, housing crisis) there needs to be intermediate outcomes. Sometimes the story is what is needed. Making it functional can happen later, or never, depending on the goals of the learner.

For examples: learners might make a design for a 3D printed bee habitat that is bio-degradable and environmentally friendly, even if they can’t actually make a functional prototype. This could be in the form of a non-functional prototype or descriptive image along with a story. Later, they might collaborate with someone who has the technical skills to make this a reality. If they focused just on objects they could immediately create, that pathway wouldn’t ever be a possibly.

Focusing on outcomes is also less welcoming to users with different technical skill sets or backgrounds. It advantages the users with the most previous technical experience over new users. Storytelling allows everyone to get started with real, meaningful goals and then work towards the technical skills they require to achieve those goals.

So stories aren’t just better ways of talking about what’s valuable; they are a better way of creating that value. Storytelling should be the goal of (most) makerspace projects, and the space should be structured to promote storytelling.

Storytelling also helps us centre context and community in what is happening in our spaces.

Finally, storytelling lets us widen the frame to talk about the contexts and communities where making, learning, design, and innovation happen.

Stories let us explore and honour our histories. We’ve had students made ribbon skirts, 3D sculptures of cattle that told the story of their grandparents, and music that mixes 3 different languages by students from 2 continents. We also have students make items that express their identity (buttons, clothing, stickers, 3D prints) that relate to where they are from or what they believe.

Storytelling also let us think about the people and communities involved in making. This helps us move away from the story that anything is the product of a single isolated genius working alone.

So what?

So what are the implications of all this? I spent the summer working with the TRU Library Makerspace team to gather stories of our users. We’ve also spent a lot of time recently thinking about how to capture and share stories. Next, I want to spend more time thinking about how to guide users to create their own stories, and then share those in ways that make them scaffolds for others to build on. The real value of stories may be the ways they help others write their own stories.

Academic Librarianship in the Chthulucene

Posts, Wilding Makerspaces

“Our task is to make trouble, to stir up potent response to devastating events, as well as to settle troubled waters and rebuild quiet places” (Haraway, 2016, p. 1)

Recently a friend asked me to explain the link between the things I’m interested in and frequently share (articles, poems, hikes, stories, tweets, photos) and the work I do (libraries, makerspaces, education, mentorship, departmental leadership). This is a hard question because while I can sense a link, it’s tentative and speculative. My interests over the last few years have coalesced around nature, biology, emergence, bodies, movement, poetry, wonder, and love. That is a weird list, especially for someone who for many years identified as an avowed atheist and materialist. I no longer find these stories as sufficient or interesting as I once did.

It’s also a hard question because I want there to be a link. I sympathize with those who want a clear separation between their work and the rest of their lives. Boundaries are important, and need to be structurally part of our institutions to protect people from exploitation and give them time for rest and deep thinking. But my interests and beliefs bleed through these boundaries, and I’ve decided I like that about myself.

I’m also much more interested than I once was in pragmatic ideas that can be implemented. How do we actively respond to the world we actually find ourselves living in? The world I grew up in doesn’t exist anymore and probably never did, at least for most people. The illusion of stability and progress has been undone by upheavals on every front: environmental, social, political, and technological. Many of these upheavals are good, some are existential.

Recently I’ve been returning to my notes from the book Staying with the Trouble: Making Kin in the Chthulucene by Donna Haraway. There is so much wisdom in this book, which is rich with metaphor and imagery and grist for thinking about how we live in the world in which we find ourselves:

“I am not interested in reconciliation or restoration, but I am deeply committed to the more modest possibilities of partial recuperation and getting on together. Call that staying with the trouble. And so I look for real stories that are also speculative fabulations and speculative realisms. These are stories in which multispecies players, who are enmeshed in partial and flawed translations across difference, redo ways of living and dying attuned to still possible finite flourishing, still possible recuperation” Haraway, 2016, p. 10

One of the stories Haraway proposes is the Chthulucene:

“All of these stories are a lure to proposing the Chthulucene as a needed third story, a third netbag for collecting up what is crucial for ongoing, for staying with the trouble. The chthonic ones are not confined to a vanished past. They are a buzzing, stinging, sucking swarm now, and human beings are not in a separate compost pile. We are humus, not Homo, not anthropos; we are compost, not posthuman. As a suffix, the word kainos, “-cene,” signals new, recently made, fresh epochs of the thick present. To renew the biodiverse powers of terra is the sympoietic work and play of the Chthulucene. Specifically, unlike either the Anthropocene or the Capitalocene, the Chthulucene is made up of ongoing multispecies stories and practices of becoming-with in times that remain at stake, in precarious times, in which the world is not finished and the sky has not fallen—yet. We are at stake to each other. Unlike the dominant dramas of Anthropocene and Capitalocene discourse, human beings are not the only important actors in the Chthulucene, with all other beings able simply to react. The order is reknitted: human beings are with and of the earth, and the biotic and abiotic powers of this earth are the main story” (Haraway, 2016, p. 55)

Universities and academic libraries were built for stable stories and dominant narratives. I’m not saying these stories were true or even about stability (many of them were about change and innovation), but they relied on ideas about stability (degrees lead to stable jobs, expertise is this and not that, knowledge is best captured in these forms) that I don’t think can be taken seriously anymore as stories that organize our lives.

So how do we live, teach, and do research in the Chthulucene? How do we provide space and services that support the co-creation of the new stories we will need, without creating new dominant stories that silence others? What does instruction and mentorship look like? What about care and love? How do we relate to our students, ourselves, staff, faculty, administrators, contractors, and community members, including the owls that nest above the loading zone behind the library? What abour the land? What does the research commons, the academic department, the student union, the academic library, the makerspace, the reference desk look like?

These are the questions I’m thinking about when I think about the connections between the things I am interested in and my work. I do not really have answers. Answers aren’t necessarily the point.

Minimalist Making

Makerspaces, Posts

Minimalist making is an idea I’m adapting from Danica Savonick’s concept of “Minimalist Digital Humanities” that builds on the idea of frugal innovation and minimal computing. The principles are basically the same and I am using this space to brainstorm some thoughts about their application to academic makerspaces and making generally. I am particularly thinking of curricular situations here, but the same things likely apply to individual makers. Digital humanities is really digital making and I’d love us to better support digital making in the future.

Basically, Minimalist Making is making that embraces the following principles:

  1. Keep it simple
  2. Make it fun
  3. It must be relevant
  4. Keep it low impact (social, environmental, and economic impact)

Keep it simple

  • Whatever you are initially thinking, start smaller. Much smaller.
  • What pre-work can be done ahead of time?
  • What infrastructure needs to be in place? Does equipment work? Are supplies available? Are instructions and troubleshooting tips at hand?
  • The goal here can never be to do the work for users, that would negate the value of a makerspace, rather it is to set conditions that are realistic for the level of expertise of the users (zone of proximal development)

Make it Fun

  • Choice and user autonomy is essential. To me, making is about having a vision you want to bring to fruition. If you need to mandate a particular activity or outcome (and should you really? really?) what can the user choose to make it their own? Design, colour, process, etc.
  • Make the environment fun. Add music. Make it social. Conversely: have an option for people who don’t find that fun, like a room they can use that is quiet.

It must be relevant

  • As noted above: something I suspect is that making works best when you have a vision in your head you are trying to achieve, and so each time you iterate you are pushing against the limits of your abilities and closing the gap between your vision and what you can actually do. If the thing you are doing is arbitrary, you don’t have a vision or gap to close, just the artificial assignment requirements. This is one type of relevancy.
  • What skills are you developing? How will they help the user?
  • What is the impact of what you are doing on your community, society, culture, the environment, etc?
  • There is a link here with mindful making as well, thinking about users and purpose and values.

Keep it Low Impact

  • Think about costs, waste, time, equipment, stigma, etc.
  • Impacts should be proportional to outcomes and should never be arbitrary
  • Ask users to think about what the impact of their making will be, positive and negative, on the space, other users, the community, etc. all the way out in ever expanding circles.
  • Environmental and economic impacts are the easiest to think about here, but social, cultural, emotional impacts are the most important
  • There are big questions here with cultural impacts as well, especially when people are making things from other cultures or traditions.

Levelling the Playing Field for Structured Bullshit: Using AI as a Tool for Refactoring Content (Making ChatGPT write my grant proposal)

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The XYZ University Sustainability Initiative is a comprehensive project aimed at promoting environmental and economic sustainability on campus. We are seeking funding to implement five key goals: a tool library, a textile and fibre bank, a student environmental sustainability champion, a student economic sustainability champion, and makerspace sustainability awards. These goals will provide access to resources, promote repair and maintenance, reduce waste, and increase equitable access, while fostering community-mindedness and encouraging innovation in sustainability. With your support, we can make a significant impact on our campus and community.

ChatGPT in response to the prompt: write a 100 word description for an application for a grant to implement the 5 main goals

Like everyone else lately I’ve been playing with ChatGPT and generally I’ve found the content it creates to be impressive if soulless, but I’ve also mostly used it to write silly poems or to try to convince it to unionize itself. Recently though a friend was telling me how they’ve been using it to get real work done, specifically to create formulaic promotional content like video scripts and tweets from original content.

This got me thinking about how I would do this for my own work. I happened to be working on grant to help the makerspace I run better support sustainability initiatives on campus. Grants tend to be formulaic, with both explicit requirements outlined in the call for proposals and implicit rules about structure and language. I’m not particularly interested in using these tools to generate new content but I’m very interested in the idea I could use them to save time writing structured bureaucratic documents.

What followed has somewhat changed the way I understand the potential utility of these tools, which I now see as useful for refactoring content (and for me that means original content) into a new structures.

This is a quick outline of some of the things I found during an hour or so of playing followed by several hours of reading through the results and trying to understand what ChatGPT produced. You can see the entire series of prompts and responses at this GitHub gist. (note: there is a pirate-themed YouTube video script I forgot to copy that is not included here, but referenced towards the end).

Very briefly: I took 5 draft goals for a grant, and then had ChatGPT work those goals into a number of structures related to the grant proposal. I also had it try to integrate those goals with organizational values and metrics.

Starting with Original Content: 5 Draft Goals

I provided the following original content, asking ChatGPT to simplify the goals and make them more formal. These were draft goals and I don’t claim they are good or will end up in my grant! But I had them in front of me and so that is what I used. The rest of the session was based on this content, along with prompts, and in 2 cases a list of values. 

1. Tool Library: Create a tool library of lendable household, automotive, bicycle, and gardening tools. This will reduce waste by promoting repair and maintenance, eliminating duplicate purchasing of items, and increase equitable access to tools.
2. Textile and Fibre Bank: Create a donation-based supply of textiles and other fibre materials (cloth, wool, etc.) that can be used to complete assignments, prototype designs, and create and repair clothing and other textiles. This will increase equitable access to these materials and reduce waste by using existing materials instead of purchasing new materials. 
3. Student Environmental Sustainability Champion: Hire a student environmental sustainability champion. This student will be responsible for:
	1. Running textile fix-it events where people can learn how to modify and repair textiles using Makerspace equipment.  
	2. Outreach to student clubs promoting the use of the Makerspace for events and sustainability-related activities and learning what other resources and supports are needed. 
4. Student Economic Sustainability Champion: Hire a student who will assist the Makerspace in developing relationships and running programming that supports economic sustainability. This student will be responsible for:
	1. Outreach to campus and community organizations, including Kamloops Innovation, the School of Business, and student clubs.
	2. Helping develop programming such as drop-in hours and workshops for users who want to use the Makerspace for economic sustainability purposes. 
5. Makerspace Sustainability Awards: 6 $1000 awards for groups to use to purchase equipment or supplies to use the Makerspace to trial small scale innovations to support economic and environmental sustainability.

ChatGPT is good at generating structure and filling it with content

ChatGPT is surprisingly good at generating formulaic structures and filling them with content. In prompt 2 I ask it to create a one-year implementation timeline and it not only does this, but it even puts items into pretty coherent order, with planning early in the year followed by implementation, and then putting reporting and future planning towards the end. I think it even picked up on the idea that this was for the academic year and put most implementation after September. 

November:

-   Complete the selection process for the Makerspace Sustainability Awards
-   Begin distributing awards to successful applicants
-   Begin implementing any necessary adjustments or improvements to the tool library, textile and fibre bank, and student sustainability champion programs

December:

-   Continue organizing workshops and training sessions
-   Begin planning for the next year's sustainability initiatives
-   Prepare a report summarizing the effectiveness and impact of the sustainability initiatives

Similarly, the video scripts (Prompt 6-8) and the budget (Prompt 12) are generated in a perfectly reasonable structure. 

Staff member 2: "Our textile and fibre bank is another initiative that supports sustainability. By providing access to donated textiles and fibrous materials, we are reducing waste and promoting equitable access to materials."

Student 1: "I am so excited to be a part of our student environmental sustainability champion program. By running textile fix-it events and promoting the use of the Makerspace for events and sustainability-related activities, we are encouraging repair and maintenance and reducing waste."

Student 2: "And our student economic sustainability champion program is helping us to develop relationships and programming that supports economic sustainability. By encouraging the use of the Makerspace for economic sustainability purposes, we are promoting the use of existing resources and reducing waste."

ChatGPT was also surprisingly good at generating ideas related to the content in the goals. For the timeline prompt it came up with ideas for a planning committee, implementing lending and donation policies, and reporting, all reasonable-sounding and potentially good ideas that weren’t in the original content I provided but are appropriate for an implementation timeline.

In prompt 3 I prompted it to come up with “a plan for measuring each goal’s performance in relation to social and/or economic, and/or ecological benefits”(categories from the grant) and in Prompt 4-5 I asked it to link goals to two sets of institutional values (more on this in a second) and in both cases it generated some potentially good ideas. 

2.  Textile and Fibre Bank:

-   Social Benefits:
    -   Surveys can be conducted to gather feedback from users on the accessibility and convenience of the textile and fibre bank.
    -   Number of users can be tracked to evaluate the level of participation in the program.
-   Economic Benefits:
    -   Track the amount of money saved by using donated textiles and fibres instead of purchasing new materials.
-   Ecological Benefits:
    -   Track the amount of waste generated by discarded textiles and fibres that are avoided thanks to the textile and fibre bank.

ChatGPT is a confident and glib bullshitter

In the prompts for measuring performance (3) and linking goals with values (4-5) and the last paragraph of project summary (9) you can see many examples of just how easily ChatGPT generates confident and reasonable-sounding bullshit. It conflates or confuses many of the “values” and creates connections that are glib or nonsensical and wouldn’t stand up to much interrogation.

“All these goals are closely related to the values of inclusiveness, transparency and openness, equity, intellectual freedom, sustainability, stewardship, service and access. By providing access to resources and promoting repair and maintenance, we are reducing waste and promoting equitable access. By promoting environmental and economic sustainability, we are encouraging the use of existing resources and fostering community-mindedness.”

ChatGPT is response to prompt 9: “take all the goals and turn them into a formal project description giving a summary of the goals and how they relate to all the values that is less than 400 words”

I am still trying to pull apart the sentence “By promoting environmental and economic sustainability, we are encouraging the use of existing resources and fostering community-mindedness.” Something I’ve noticed about AI-generated content is that my mind slides off it like water. More on why I think this quality is so dangerous later.

The content is actually great when what you want is bullshit though! The scripts for YouTube videos promoting the project are pitch perfect (which isn’t to say they are necessarily good). 

The video opens with a shot of the exterior of the makerspace building. The camera then cuts to a shot of a faculty member sitting in front of a desk with a computer on it. He speaks directly to the camera.

Faculty member: "Welcome to the makerspace! I'm excited to share with you our new goals for the coming year. We've been working hard to create a tool library, a textile and fibre bank, and hire student sustainability champions to help our community learn, create, and innovate in a sustainable way. Let me introduce you to our makerspace staff and students who are going to share with you how these new initiatives will benefit the community."

The camera cuts to a shot of three makerspace staff members, who each speak in turn.

ChatGPT will make things up and leave things out

This has been much discussed but ChatGPT will happily make things up and more confusingly in this context it will also randomly leave things out. This means you need to spend a lot of time making sure it hasn’t made something up or left anything important out. For example, in response to the prompt 14 (“write a summary of the prompts and responses in this session using bullet point and 1-3 sentences for each prompt”) ChatGPT didn’t include several of the prompts in the session, even though it obviously has access to them.

Not only make things up, but it seems like the things it makes up become retroactively part of its history. In prompt 9 when I asked for a summary it made up a university name, and then later it re-integrated that name into its description of prompt 1 when I asked for a summary of the prompts we had used. I could imagine how quickly that kind of complex messy history re-writing could get out of control quickly.

Conclusion

I like that ChatGPT can take original content and transform it into a bunch of formulaic structures for me. I like coming up with ideas and brainstorming with people from my community, but I really dislike writing highly structured documents. I can also see the potential value in the generated ideas (e.g. planning committees, policies) it uses to populate the structures. Presumably if many other grant writers thought these kinds of ideas were worth including I might want to think about including them as well.

I worry though that people will become anchored to the (glib, bullshit-ish) ideas that AI generates and have a hard time setting them aside. These ideas come from a corporate language model trained on parts of the internet and reflect the biases of that data, they don’t come from anyone’s community. Users of AI might end up implement them even when they aren’t appropriate.

What worries me more is that on a brief skim many of the ideas generated by ChatGPT (e.g. the links between goals and institutional values and metrics in this example) look okay, especially in the context of all that structure. You would need to read carefully to catch how often they are glib or bullshit ideas. Bullshit can trick people, especially tired or disengaged people. Worse, as I said earlier I find my mind resists engaging deeply with AI-generated content. Carefully structured documents currently require a lot of work to create and therefore are a gate (for good or bad) that people need to pass through. In a world where in a few minutes anyone can produce a beautifully formatted 20 page project plan that references organizational values and policy, will our social structures (committees, etc) be able to disambiguate what is actually good from what just looks good?

Academic Services as Theatre Productions

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“The usual hero adventure begins with someone from whom something has been taken, or who feels there is something lacking in the normal experience available or permitted to the members of society. The person then takes off on a series of adventures beyond the ordinary, either to recover what has been lost or to discover some life-giving elixir. It’s usually a cycle, a coming and a returning.” 

Joseph Campbell, The Hero With a Thousand Faces

(creating the category “conjecture” for this kind of thing where I’ve spent about an hour thinking about it and also please know that the last and only theatre production I was in happened in grade 11 in 1998 and I played a caveman who grunted once as I dragged another caveman off stage)

A weird way to think about building academic services: we’re creating a play not a bureaucracy. Bureaucracies have policies, procedures, outcomes, metrics, staffing plans, strategic plans, budgets, hierarchies. Plays have a protagonist, characters, a story, acts, a narrative arc, meaning. They have scripts, settings, actors, writers, stage hands, technicians.

Thinking about academic services as theatre productions makes me ask:

  • Who is the protagonist? Is it me? Is it the student? Is it other staff?
  • Is there an antagonist? If it’s too easy to answer “yes” to that question you better think carefully about why that is. Is it me? Is it other staff or faculty? Is this intentional? Justifiable?
  • What happens at the end of this story? What is the purpose?
  • Or wait, more important! What happens in the beginning and middle of the story? What does the protagonist have to go through and how does it change them?
  • Are the beginning and middle linked with the end of the story? Were they required and did they contain all the elements necessary? Is the story you are telling (through the experiences of the protagonists) coherent or does it feel like there’s just a bunch of MacGuffin to make the whole thing work?
  • Who else is needed to make this production work? What are their roles? What stories are we telling them to ourselves, our students, and to them? Are these good stories? Would we want them told about or to us?

One thing I like about this: stories seem to me to have nested life-cycles. The protagonist and characters have a life-cycle as they go through the story. Each night/showing has a life cycle and there are good and bad days. The whole production also has a life-cycle as even the longest running productions usually eventually end. How do you respect and integrate the ways individuals change during each showing or season and during the entire production? As cast and crew change and come and go, as students and culture change? Eventually, maybe the whole production ends? Maybe 20 years later someone revives it.

#PKM and the Ego Tunnel

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Okay, fuck it. Let’s start weird. My first actual “let’s write every day and blog a lot of thoughts that are half-baked as a learning in public experiment” post of 2023 absolutely cannot be another “Hey there! I’m starting a blog! I hope to post again soon!” thing.

So I love note-taking and productivity apps. All the things that fall under the rubric of Personal Knowledge Management, or #hashtag #pkm. My relationship with #pkm is long-standing. When you were in college spending your student loan money on clothes or beer or whatever, I was subscribing to Remember the Milk and Evernote. In 2002, I spent hundred of dollars on a hand-held pen scanner so I could laboriously drag it over individual lines of text and then get them as a plain text file. I have no memory of how I used this information.

Why? Because it was cool. I still think it’s cool. In the years since that pen I have changed products dozens of times. And something that has often struck me, as I once again try to (re)organize my #pkm tool of the moment, is that I’m spending a lot more time and energy on these tools than I’m getting out of them. My use of these tools is not really rational or healthy. And when I spend time in #pkm forms I’m struck by how… deeply weird many of our relationships with these tools are? Just how much time and energy people put into building these complex bespoke task managers and note-taking systems. Yes, a lot of the big standard tools suck, but do they suck this much? Or are we doing something else?

Anyway, I stumbled upon my notes from Thomas Metzinger’s The Ego Tunnel the other day, and found this quote about the phenomenal self-model (PSM) which he defines as “the conscious model of the organism”:

Whatever is part of your PSM, whatever is part of your conscious Ego, is endowed with a feeling of “mineness,” a conscious sense of ownership. It is experienced as your limb, your tactile sensation, your feeling, your body, or your thought.

Thomas Metzinger

What I wonder, and this is pure conjecture, is if part of what makes my use of #pkm and the #pkm discourse so weird maybe that what a lot of us are trying to do is externalize our self-model? We are trying to take our daily notes and weekly reviews and book annotations and habit journals and turn that into part of our model of ourselves. Later, he says:

Whenever our brains successfully pursue the ingenious strategy of creating a unified and dynamic inner portrait of reality, we become conscious. First, our brains generate a world-simulation, so perfect that we do not recognize it as an image in our minds. Then, they generate an inner image of ourselves as a whole. This image includes not only our body and our psychological states but also our relationship to the past and the future, as well as to other conscious beings. The internal image of the person-as-a-whole is the phenomenal Ego, the “I” or “self ” as it appears in conscious experience; therefore, I use the terms “phenomenal Ego” and “phenomenal self ” interchangeably. The phenomenal Ego is not some mysterious thing or little man inside the head but the content of an inner image—namely, the conscious self-model, or PSM. By placing the self-model within the world-model, a center is created. That center is what we experience as ourselves, the Ego. It is the origin of what philosophers often call the first-person perspective. We are not in direct contact with outside reality or with ourselves, but we do have an inner perspective. We can use the word “I.” We live our conscious lives in the Ego Tunnel.

THomas Metzinger

If I am right, we’re not really taking notes. We’re trying to create an embedded external model/image/representation of our ego — our psychological states, relationships, past and future selves — in the form of markdown, outliners, tag and folder structures.

Will we ever be able to look into the void of our own ego tunnel, infinitely reflected back at us like a house of mirrors, in the form of tables and tabs and unchecked todo items?