Platitudes: The Carbon Weight of the Post-Platform Scholarly Web
Bibliographic record
Abstract
This article interrogates the environmental consequences of our dependence on platforms, which increasingly includes higher education and the ways in which we share and disseminate scholarly research. We make a case for a minimal computing–inspired, back-to-basics approach to web design as a strategy to push back against the hegemony of big tech and adopt more reflexive, slow, and eco-conscious forms of knowledge production. At the same time, we are open about the trade-offs of deplatforming a scholarly project, using the authors’ experience creating the University of Alberta SpokenWeb website as a case study. The University of Alberta is part of the SpokenWeb Network, a Social Sciences and Humanities Research Council (SSHRC)–funded network that aims, among other things, to showcase local collections of literary sound. The University of Alberta’s own archive, which dates back to 1957, features sound performances, interviews, lectures, and radio shows made by visiting authors and captured on reel-to-reel and cassette tape. When creating the project website, the team wanted to take a more hands-on approach, using a lightweight, static site design, which was inspired by the “needs-based” critical praxis of minimal computing (Risam and Gil 2022, 6). The challenge, as we found, was in how to negotiate sustainability in terms of carbon cost and the long-term maintenance and care of the archival materials, which for us meant finding ways to bridge between our digital project website and the existing University of Alberta library infrastructure. Along these lines, some of the key questions our article engages with are: How do you measure the carbon impact of a digital project? What practical steps can researchers take to design (or redesign) a website to minimize the energy cost? How might moving away from platforms to static sites change the labor distribution, in terms of how sites are maintained, updated, and preserved?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".