except for this song, it is perfect

I’m sure it’s not just me, but it definitely seems tough out there, right? Leaving the destabilizing world of geopolitics out of it, it seems like everyone I know is going through something these days. Frequently cited Academic Scholar Smash Mouth (1999) really did say it best when he said “the years start coming and they don’t stop coming.”

I’m quite aware that recency bias tells us this is the worst it’s ever been. And the thing about worst is that it’s relative. No matter how much it would give us psychic pleasure to do so, quantifying worst probably doesn’t do anyone any good. Perhaps a more worthwhile endeavor is the understanding that each of us has a very different set of situations, experience, and biology. Several years ago, as I lay on the floor of a lunch meeting with sciatic pain from a herniated disc, I was told that I’d really know what pain was if I had ever given birth, and that I was actually lucky in this moment. And maybe that person was right. There is a good likelihood that a small taste of birthing would have been a fresh perspective, one that freed me from my attempts at back pain relief on the floor of that conference room. But acute and chronic pain are two completely different things, and in that moment I left feeling that A) I couldn’t win (take time off and be lazy/weak, or fight through and be weak/dramatic), and that B) someone else would always have it worse. But I don’t want to be the worst. In fact, I spend most of my time thinking about how to make things better. And as another adage goes, comparison is the thief of joy – and I think that goes just as much for comparing hardship.

Students live inside that kind of comparison all day: marks, rankings, the classmate who finished first. So I don’t blame them for taking the quickest path, which these days is a chatbot. When a curriculum values breadth over depth, there’s always another thing to hand in, and the work becomes something to finish and never think about again. A student who treats it that way has missed the point of learning, but the system taught them to.

I’m not sold on AI being the all-powerful entity that will destroy humanity. Cory Doctorow’s argument, that the stories of awe-inspiring terror are mostly a ploy to justify sinking unimaginable sums into projects that are about to go public, is more persuasive to me. But in my daily grind as a high school teacher-librarian, I do wonder what even the non-Doomsday outcomes mean for student learning and life opportunities.

When it’s not Minecraft open on one of the library’s PCs, I’d say that approximately 1/3 of the time I see some variant of an AI tool open and being engaged with – mostly ChatGPT and the automated Google AI response. Heck, even I struggle with it – the quality of search engine responses has declined so much in the past several years that I will occasionally find my way over to Claude for help finding a useful site. I worry about this for a couple of reasons. First, the AI we are getting now is going to hit a point where, to defend each company’s bottom line, it gets degraded with advertising and reduced in quality to keep you engaging with it. See planned obsolescence in light bulbs, or the enshittification (another Doctorow-ism) of Google. This is the most free it will ever be, and because these systems are proprietary, we’ll have no say when that changes. Second, the more we lean on it, the more we lose the skills needed to exist without it.

And AI is basically catnip to anyone. All of us feel short of time. All of us have things we would rather be doing, and often we feel like we are just jumping through the hoops of our responsibilities. And yes, there is a place for following procedure and i-dotting and t-crossing. But when AI makes most of the assignments we have been handing out over the past 20+ years irrelevant, we have to ask what we were using those assignments as a proxy for.

What is our goal? I think it has to be development and growth of students – not to some standard and a mark on a bell curve which probably reflects socioeconomic status better than growth – but to people who know their own strengths and weaknesses and know how to develop onwards. One of the most frequent arguments against changing how we mark and assess is that marks motivate learning (they do for some, though they demotivate others), and that dropping them lowers standards and flattens growth and excellence. But is that really the case? I think about “gifted” kids who make their way through elementary and secondary school without much effort, only to struggle in a world that doesn’t rely on tests and grades. What if, to pass a grade, they had to show growth and development beyond the limits of the curriculum? That’s not a lower standard, just a different one.

This past August, MIT released the Report of its Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. Several of its ideas, I think, should shape our thinking well before post-secondary education. Three stood out:

• Shifting assessment away from work marked solely on the product, toward oral exams, semester portfolios, and out-of-class work paired with in-class discussion
• Building structured in-person social learning into every subject, with check-ins and graded discussion
• Floating, as a thought experiment, the idea of going gradeless, since without grades many of the incentives to cheat might disappear

We’ve always had an assessment problem. In the world before AI, product had its issues too – think of the middle school science project that was very obviously made by the parents. But now that a finished-looking product is accessible to everyone, this is a good time to ask what we were trying to assess in the first place.

This will require shifts in our curricula, but we can’t afford to wait for that to happen because we are moving at a pace about 20x faster than curriculum development (and honestly that’s probably generous).

So I ask you: in your subject area, what skills are the crucial ones – the things that Chemistry, or Grade 3 Science, or Grade 11 History couldn’t be without? Try to set aside your love of your subject, and focus on the things that will impact the life of a student who may not be exposed to those ideas again. What are your five?

If you have your department or colleagues teaching the same class – compare your notes and make your case. How would you see a student doing this, with or without AI in the room? What does success look like, and how can you gather evidence without relying on product?

Oh, and if anyone with policy or budget power is reading this: we need help with traditional literacy skills in high schools. I know early investment gives the best return, but the gaps are real, and these students deserve better. A magical word generator doesn’t mean much when you don’t understand the words.

alternate title: Morgan can connect almost any topic back to cows

I grew up with cattle, a herd animal. You can’t keep a single cow by themselves – not if you want it to be happy. As a farm kid, summers were cattle show season, and while most went off to green pastures the show stock always had a few others stick around in the small pasture off the farmyard to keep them company (and to prevent escape artists). While humans have adapted to independence more than our bovine brethren – probably because of our positions as predators – we are still meant to live with other humans. We do live in a society, after all. We are completely reliant on our caregivers for much of our childhood, and we’re kidding ourselves if we don’t admit that it carries into our adulthood as well. Solitary confinement (a misnomer if there ever was one) is considered cruel and unusual punishment.

To me, the difference between solitude and loneliness, must be the product of the quiet, forced contemplation of the hunter – a time to gather one’s thoughts and sit in the silence required to hunt large prey – so that you can come back to your people with both food and as a better member of your society. Solitude centres us. The voice of loneliness is one of failure though. Being cast away has been a traditional societal punishment, from Greek ostracism to the British penal colony of Australia and the Gulags of Stalinist Russia, or the LGBT child exiled from their homes or sent to conversion therapy. The message? Others don’t want you around. You have no value. Others are better off in your absence.

Solitude is empowering. Loneliness is not.

One of the most interesting things about the time we live in is how our technological structure enforces loneliness, but not solitude. The very real fear we have of judgement means that the things we would not be willing to express in a classroom or work conversation, many of us will say through the distance of our device screens. Thus, we both feel not alone, and tear up the social contract of being a member of a group of people at the same time. And if the only place we can be our true selves is removed from the people themselves, that strikes me as the loneliest thing of all.

a reflection on the end of an era

Yesterday I handed in my last assignments for my last course in my Master of Educational Technology degree through the University of British Columbia. While I won’t graduate until May, it marks the end of an era – just shy of six years I have been plucking away at courses through UBC, first with my post-Bacc in Teacher-Librarianship, and then this program. Honestly, it probably won’t sink in until this January when the next semester begins and I am not juggling the demands of my day job and academia (and supervising the Varsity Boys Volleyball team, haha).

I am thankful for the learning of these years; the ideas and architecture of teaching, learning, and technology that have been made visible through thinkers and makers with skill much greater than my own. At least once a week I am reminded of the meme posted below, and when I pull back from my own unique situation, lenses, and experiences, I continue to be humbled by all of the things I know I don’t know, and even more so the things that I don’t know that I don’t know.

One thing I wish I’d had throughout this degree — and especially in the final projects — was a small community of co-designers. Working full-time while studying part-time often meant creating in isolation or across time zones and distance, without the iterative conversations that sharpen ideas and reveal blind spots. Learning is ecological, and I felt the absence of that ecology at times. For anyone thinking about MET, I strongly encourage it, but especially for you to take the financial hit and take as many summer institute classes as you can. It was these experiences that really got me keen on the importance of hybridity. Even so, the work felt like a reminder that none of us should be designing the futures of education alone.

The more I learned, the more I realized that technology is never neutral — it always arrives embedded with assumptions, values, and power. I came into this program as a tech optimist, but leave it as a tech realist. This kind of program (both the T-L one and the Ed Tech one), would not have been possible to me given my geographic location. Such programs don’t exist here in Manitoba. This program was a gift — but one that came wrapped inside an increasingly precarious technological landscape. However, to borrow from the words of Cory Doctorow, the enshittification of technology has hollowed out some of my previous optimism. When so much of the truly equitizing and democratizing potential of technology is hidden behind paywalls and gradually whittled away even then for increasing add-ons you begin to wonder if the world-views of yourself and the architects of these applications are in alignment.

As a teacher-librarian trying to think carefully about ed tech, I see the role of libraries, both public, elementary/secondary school, and university as a solution to some of these challenges. This will require sustained public funding, and the (mostly) corporations that design these materials to subsidize access to ensure these technologies don’t become yet another divide between the have and have nots. It is not hard to imagine a future where the ability to participate in digital culture depends entirely on one’s ability to pay for the tools required to access it. Perhaps this is no different than inequities within print culture — but it still runs counter to the vision of education I was raised in, both as a student and later as a teacher.

I also hope that the notoriously slow-moving systems of curriculum-development and education are focused on what strikes me as the most crucial adjustment necessary of the LLM age – assessment. Our paradigm has shifted almost instantaneously, and our previous methods of assessing work at its conclusion feel unsuited for the times we are living in. Honestly, it was never suited – but it was easier, and for the students who were able to submit a polished product thanks to the assistance of a tutor, parent, or other support, their ‘knowing’ was never assured in the time before. I hazard to say that it was our biases about who specific learners are, their backgrounds, and what they look like (plus maybe the discomfort in challenging involved families) that made us look the other way. AI didn’t break assessment — it revealed what was already broken.

While I worry that AI will mean increasingly high classroom numbers, I actually think that it calls for more teachers, more human mentorship, more conferencing and check-ins along the way – a necessity that is almost impossible in our current set-up. If AI teaches us anything, it’s that students don’t need us less — they need us differently.

Imagined as a rousing political speech, with patriotic music slowly swelling in the background.

Colleagues, I know many of you are excited about NotebookLM, especially that uncannily almost-human podcast feature. We upload our readings, videos, and professional documents, then receive instant synthesis supporting multimodality and differentiated instruction. But I want us to consider what’s happening to our professional expertise when we adopt this tool—or any LLM-based assistant. We’re witnessing the remediation of educational expertise itself, transforming teachers from knowledge-holders into knowledge-brokers. NotebookLM stands out for grounding its responses in uploaded materials, lending its outputs an authority that masks their mediation.

To understand what’s at stake, let me introduce a concept from media studies: remediation. NotebookLM remediates the entire research apparatus of teaching—our file cabinets, OneDrive folders, annotated textbooks, and accumulated professional wisdom. Bolter and Grusin (2000) argue that remediation occurs through networks of formal, material, and social practices:

Formally, it remediates the academic literature review, the planning notebook, even Socratic dialogue; but promises “complete and comprehensive access to information” while obscuring the interpretive labor that transforms information into knowledge (Papacharissi, 2015).

Materially, it replaces physical artifacts of teaching expertise (marked-up curriculum guides, annotated student work, scribbles in margins) with algorithmic processes that appear transparent through source citations yet are hidden behind algorithmic choices. NotebookLM produces what Bolter and Grusin (2000) describe as hypermediacy (visible layers of mediation like source links, formats, AI voices) that paradoxically create a sense of immediacy and authority rather than critique.

Socially, it remediates us as expert practitioners. When we upload materials and receive instant analysis, our professional authority shifts from knowing to prompting—a different kind of expertise entirely.

Goodbye Inquiry, Hello Output

Linguist Adam Aleksic (2025) argues that “truly knowing an answer requires struggling with uncertainty.” Consider planning a unit on New France in Canadian history—a unit Manitoba students often struggle to find relevant. Traditionally, this required understanding primary sources, synthesizing across texts, connecting to standards, curating materials, anticipating misconceptions, designing meaningful assessment.

NotebookLM generates all of this in seconds. But as Aleksic describes, “with each additional abstraction from uncertainty, the easier it is to find answers, and the more confident those answers sound.” The tool produces seeming pedagogical expertise with the “aura of truth, objectivity, and accuracy” that danah boyd and Kate Crawford (2012) identify in Big Data mythology.

Yet can we explain why these particular connections matter? In philosophical terms: do we know what NotebookLM claims, or merely believe what it tells us?

The Question Behind the Question

Aleksic describes how “the lost ritual of asking has collapsed the meaning of the question in the first place.” When we can instantly generate unit materials, we never wrestle with fundamental questions: Why teach about New France? What should students understand? How does this connect to their lived experiences?

These aren’t questions NotebookLM can answer. They require what Haraway calls “critical, reflexive relation to our own practices” (as cited in Papacharissi, 2015). The tool can synthesize curriculum documents but cannot interrogate why we chose those documents, what we’re unconsciously prioritizing, or whose perspectives remain absent.

As Aleksic (2025) writes, “figuring out which question to ask is more important than the answer itself.” But NotebookLM’s efficiency makes all questions appear equivalent. We’re “drowning in a sea of answers, forgetting how to ask the right questions.”

Papacharissi (2015) captures this perfectly: AI outputs “oscillate between the unbearable lightness of information and the impossible gravitas of knowledge.” NotebookLM offers comprehensive information access but cannot deliver genuine pedagogical knowledge; the heavy weight of knowing that emerges only through sustained engagement with uncertainty.

Colleagues, I’m not asking us to abandon NotebookLM, but let’s use it differently. Treat its outputs as another text to interrogate, not authoritative synthesis. Our students need us to model what it means to genuinely know, not merely retrieve.

References

Aleksic, A. (2025, December 3). the importance of not knowing. Substack.com; The Etymology Nerd. https://etymology.substack.com/p/the-importance-of-not-knowing

Bolter, J. D., & Grusin, R. (2000). Remediation : Understanding new media. MIT Press.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Papacharissi, Z. (2015). The unbearable lightness of information and the impossible gravitas of knowledge: Big Data and the makings of a digital orality. Media, Culture & Society, 37(7), 1095–1100. https://doi.org/10.1177/0163443715594103

Everything interacts. Always. We shape it. It shapes us. It’s all a big soup.

As a teacher-librarian, I’m constantly making decisions about which databases to subscribe to, which search tools to recommend, which encyclopedias to point students toward. These decisions often get framed as “neutral” by just providing access to information, offering students “the right resources.” But are they?

This question started nagging at me during IP 2 where I analyzed software encyclopedias through McLuhan’s tetrad and Actor-Network Theory. I decided to test something simple: I searched for two controversial topics across different encyclopedia subscriptions our division provides to students. The results weren’t just different—they were fundamentally different.

I sat there staring at two browser windows, and something clicked: this wasn’t a bug. This was a feature. Each platform was enacting a specific epistemology, a particular idea of what knowledge is. And my choice (as a librarian, and as someone who shapes student access to information) wasn’t neutral at all. I was choosing between worlds, while selling the guise of neutrality.

Why start with search?

When it came time to get to brass tacks on this assignment, I knew I needed an entry point that was practical. Not abstract. Not Barad discussing quantum entanglement — even though it’s fascinating.

Because it seems to me like if we want people to think outside of the box, we need them to realize that the tools they hardly think of as technological have been quietly organizing how knowledge appears to us for a very long time. They’re quietly working in the background; and their output exposes what’s going on behind the scenes. I think this is what makes them a great place to start unpacking the complexity of ideas behind new materialism.

How the elements came together

So I designed a professional learning activity: choose a heated topic, search it in three different tools (Google, Wikipedia, TikTok), and compare what appears. Then unpack: How does each tool assemble knowledge?

I think the session would ultimately take about two hours to work through with a group, but could probably be done in an hour and a half. I have embedded audio files into the presentation with my speakers notes, but have also linked them here if you would rather read them. My presentation slides are directly below.

If you’re an educational technology specialist, a teacher, an administrator or if you make decisions about which tools students use, which platforms teachers adopt, which systems organize learning, I’m inviting you to do something simple:

Pick a controversial topic. Search it in three different places. Compare what appears.

Then ask: What differences did this technology create?

It’s not a complicated activity. But I think it’s a critical and worthwhile one.

Because once you see how Google, Wikipedia, and TikTok assemble knowledge differently, you can’t unsee it. And that’s where the real work begins.

Not in finding the “right” tool. Not in establishing “best practices.” But in developing the literacy to read how tools shape what we can know, and the responsibility to choose—and keep questioning our choices—accordingly.

An assignment in which I didn’t quite follow the instructions properly, but came away with a greater understanding because of it. This video was made with the help of Adobe Podcasts and additional images and text were added in CapCut.

References

Black, E. (2001). IBM and the Holocaust : The strategic alliance between Nazi Germany and America’s most powerful corporation. Dialog Press.

Franklin, U. (1989, November 6). The real world of technology: Part 1 [Lecture]. CBC Massey Lectures. https://www.cbc.ca/radio/ideas/the-1989-cbc-massey-lectures-the-real-world-of-technology-1.2946845

Franklin, U. (1990). The real world of technology. House of Anansi Press.

Illich, I., & Sanders, B. (1988). ABC: The alphabetization of the popular mind. York University Press.

Wikipedia Contributors. (2021, December 20). Ursula Franklin. Wikipedia. https://en.wikipedia.org/wiki/Ursula_Franklin

a study of @etymologynerd

When I read this assignment outline, I immediately thought of @etymologynerd. Adam Aleksic’s posts are uniquely meta. He explains the history of our spoken and printed words and shows how that history is shaped by the media we use. In doing so, he invites viewers to understand how we’re shaped by language, how language shapes social media, how social media reshapes us and our language, how we shape the media itself – and how it all comes together to capture our attention. His videos use the same rhetorical hooks and algorithmic tricks that keep people scrolling, but he also exposes those mechanisms, breaking down the walls of manipulation to make them visible.

Aleksic is a Gen Z Harvard linguistics graduate. As a high school student, he started an etymology blog and became a prolific Reddit poster, learning how to game a much simpler algorithm than we face today (Peterson, 2025). After earning his BA in Linguistics, he shifted into short-form video in 2023. By 2024 many of his videos regularly surpassed one million views. Today he has over 800,000 followers on TikTok and more than one million on Instagram, where his posts often circulate beyond each platform’s walls. I first encountered his “dolphin language” series while not even using TikTok.

Although he keeps his following list small, the accounts he does follow map onto his niche interests: other linguistics educators (@linguisticdiscovery, @lingonardi), academically adjacent creators (@magic.c8.ball, @astro_alexandra), and quirky internet-culture archivists (@depthsofwikipedia). These affiliations reflect the tone that defines his work: grounded and absurd.

That blend is especially visible in the dolphin videos, which offer a helpful starting point for analysis (Fig. 1).

Bolter and Grusin (2000) define a medium as something that remediates. Or rather, it refashions the social significance and techniques of previous media forms into something new (p. 45). We see what would traditionally be a written linguistics exercise transformed into a multimodal micro-lesson and performance. The linguistic concepts in the video are remediated through absurdist humor: a parody that simultaneously entertains and instructs. It is a modern take on the joke about comma usage between the phrases ‘Let’s eat, Grandma’ and ‘Let’s eat Grandma’. Despite the absurdity, the underlying lesson remains intact: languages are built from arbitrary rules. This “theory by parody” makes complex linguistic concepts legible to a broader audience by turning exposition into performance. The humor functions pedagogically: Aleksic embodies the rule rather than explaining it. Ultimately, the clip demonstrates Bolter and Grusin’s logics of immediacy and hypermediacy. Immediacy appears in the illusion of direct communication and the sense of transparency created through voice, sound, and viewer address, while hypermediacy emerges in our constant awareness of the medium itself—through captions, editing cuts, and the dolphin-language grammar chart superimposed behind him (pp. 71, 81–82).

That being said, no one is going to watch this video and come away able to create their own conlang. This is a hook rather than a full lesson—an entry point that sparks wonder rather than mastery. It is engagement through wonder, a perfect example of affective pedagogy. The brevity and interactivity turn learning into an invitation; the comment section, duets, and follow-ups become the extended classroom. It makes linguistic systems accessible to people who might never encounter academic linguistics. In this sense, it self-remediates and thus translates scholarly explanation into algorithmically optimized curiosity. One has to wonder, to borrow from Latour’s Actor–Network Theory, how these technological prescriptions (such as brevity, performance, and engagement incentives) will ripple out into the future of learning, shaping not only how knowledge circulates but how attention itself is disciplined (Latour, 1988).

Aleksic is aware of the attentional demands placed upon him by the medium. Like many, his videos are known for their fast pace and use of an influencer accent. In his recent book Algospeak, he describes it as a form of uptalk in which there is a rising tone on a stressed syllable with continued high tones until the completion of speech. This emphasis serves to hold a viewer’s attention (Aleksic, 2025). It is speech, optimized for the algorithm.

This is an example of how retention-rate metrics shape the TikTok ecosystem. In fact, “retention rate” appears on nine out of the book’s 220 pages (Aleksic, 2025, p. 242). He explicitly references his use of trending terminology as video frames because of how it impacts engagement metrics, stating that many topics interest him—but that he knows they will not bring the same viewers as trending ones. This leads to videos like the following, which harness trending brainrot and memetic terminology to introduce the concept of the use-mention distinction (Fig. 2).

Instead of choosing to focus on academic and abstract examples, @etymologynerd moves into popular and concrete spaces. The algorithm itself begins to co-author meaning: language is a performance, a signal of in-group belonging, and an object designed to optimize metrics. In this video, linguistic meaning shifts from semantic work (referring to something in the world) to social work (signaling a shared cultural context). The reference is the joke, and thus it becomes meta-communication about referencing the reference. His delivery shows his awareness of how the algorithm scripts his choices, but the content itself shows what the platform is scripting for all of us. TikTok remediates language from a communication tool into a participation badge – a way to show that you belong to the network. Educational theory becomes a theory of belonging, not just understanding.

But TikTok also dis-enables important dimensions of learning. The rapid pacing, short runtime, and endless For You scroll reward quick consumption rather than slow thinking. Reflection is costly on a platform where attention is the primary currency. Videos can be difficult to relocate later unless a viewer follows the creator, which makes deeper engagement fragile and fleeting. Misinterpretation rises when nuance is compressed into punchlines and edits.

Citation (Fig. 3) poses a distinct challenge. Aleksic makes a noticeable effort to visually cite academic sources on screen, but those references disappear as quickly as they appear. They cannot be clicked, traced, or peer-verified in the moment. The platform’s ephemerality means that sources, while visible, remain challenging to verify. In this way, TikTok pushes creators to monetize attention and charisma over ideas. The medium privileges credibility-as-aesthetic over credibility-as-evidence. It also leads to potential misuse and abuse as poor-faith actors use them to build credibility.

In my estimation, as an educational tool, TikTok is mostly hook, but no line. It can lead to new understanding, and Aleksic’s work feels like a kind of harm-reduction approach to academic thinking: a way to keep deeper ideas alive within the feed. As Latour notes, technologies prescribe actions and competencies in advance, imagining a user who will behave as scripted. Yet real users often comply partially, resisting or reshaping those prescriptions. He describes how networks of aligned set-ups form what Waddington called a chreod—a necessary path that silently channels users toward the expected behavior (Latour, 1988, p. 308). TikTok’s chreod rewards speed, humor, outrage, and referential belonging. Real learners may diverge, but the medium continually nudges them back onto that narrow track of attention. To land a big fish, we need the hook of attention and the line of sustained learning. Hopefully, too much of one doesn’t destroy the other.

References

Aleksic, A. [@etymologynerd]. (2023). #conlang #language #linguistics #dolphin #harvard #greenscreen [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7544794226249321741

Aleksic, A. [@etymologynerd]. (2024). words become funny because they are words that are funny #etymology #linguistics #language #hawktuah #skibidi #philosophy #logic #semantics [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7421288562651319582

Aleksic, A. [@etymologynerd]. (2025). BOO 👻 #semiotics #culture #halloween #costume #sociology [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7562712621909019917

Aleksic, A. (2025). Algospeak. Knopf.

Bolter, J. D., & Grusin, R. (2000). Remediation : Understanding new media. MIT Press.

Johnson, J. (1988). Mixing humans and nonhumans together: The sociology of a door-closer. Social Problems, 35(3), 298–310. https://doi.org/10.1525/sp.1988.35.3.03a00070

Peterson, A.H. (Host). (2025, September 10). How algorithms are changing the way that we talk [Audio podcast episode]. In Culture Study Podcast. https://podcasts.apple.com/ca/podcast/how-algorithms-are-changing-the-way-we-speak/id1718662839?i=1000725863919

Analyzing the ‘Bridge Encyclopedia’ through McLuhan’s Tetrad and Latour’s Actor Network Theory

I can’t be the only person who was obsessed with Microsoft Encarta’s Mindmaze game. I had always yearned for a set of Encyclopedias… but when that free disc comes with your computer, I shortly stopped asking for them.

it’s media all the way down

Media ecology is the study of how media and technology function as environments that shape human perception, communication, and understanding. It looks not just at content, but at the structures and systems we build and are surrounded by, from language to smartphones, and how they shape what we can think, say, and do. Like an ecosystem, these media interact with one another and with us, constantly reshaping our cultural and intellectual “habitat.” Media ecology asks the right questions because it recognizes that anything that impacts our communication or interaction provides limits and biases to what is possible. Without recognizing the impact of these impositions we risk mistaking the shape of our media environment for the shape of reality itself. 

This definition aligns with Strate’s (2000, as cited in Strate and Lum, 2020) argument that media ecology is a perspective; “a way of seeing”—that treats media as environments rather than just channels. Neil Postman (as cited in Lum, 2000) similarly calls media ecology “the study of the cultural consequences of media change that affects our social organization, cognitive habits, and political ideas” (p. 4). In other words, it is not enough to examine what media say; we must examine what they make possible and what they make difficult to imagine. 

Strate and Lum (2000) situate media ecology within a tradition that is ecological, interdisciplinary, and activist, drawing on Patrick Geddes’ view that intellectuals must act as shapers of environments, not just observers (p. 60). This implies that media ecology cannot belong neatly to one discipline. It is necessarily intersectional, combining insights from communication studies, history, sociology, semiotics, and philosophy. Thinking ecologically means remembering that, as Miller (1989) puts it, “no living organism can be understood except in terms of the total environment in which it functioned” (as cited in Strate & Lum, 2000, p. 68). 

Mumford’s historical schema illustrates how environments structure society over time. His three phases—eotechnic, paleotechnic, and neotechnic—mark shifts from renewable energy to industrial extraction to machine-human symbiosis, with each stage reorganizing labor, power, and social classes (Strate & Lum, 2000, pp. 63–65). These phases demonstrate that technological environments are not neutral: they define the “rules of the game” for entire cultures. 

Lum (2000) further explains that media have both physical and symbolic dimensions, each with distinct biases. These biases can be temporal, spatial, sensory, political, social, metaphysical, or epistemological (p. 2). To do media ecology is to surface these biases and ask what forms of knowledge, attention, and relationship they enable—and which they foreclose. This is why media ecology “asks the right questions.” It does not merely catalog media; it interrogates how each medium privileges some possibilities and silences others. 

Model of Educational Media Ecology

References

Lum, C. M. K. (2000). Introduction: The intellectual roots of media ecology. New Jersey Journal of Communication, 8(1), 1–7. https://doi.org/10.1080/15456870009367375

Strate, L., & Lum, C. M. K. (2000). Lewis Mumford and the ecology of technics. New Jersey Journal of Communication, 8(1), 56–78. https://doi.org/10.1080/15456870009367379