Bibliographic record
Abstract
This essay examines how many scholars—including myself—are thinking and feeling about growing concerns about the climate impacts of digital networks. Whether in news headlines, civil society reports, or peer presentations, we increasingly encounter alarming figures that link streaming video and cloud storage practices with a potential carbon time bomb. As a result, an eclectic range of personal behaviors have blossomed that seek to acknowledge and respond to these potential harms, including digital land-energy acknowledgements, low-res aesthetics, conspicuous non-consumption, and media arts attempts to prefigure greener futures online. These digital environmental actors may lack a clear account of the relative impacts of a given gesture, but are nevertheless motivated by a strong sense of urgency and responsibility to modify the means by which they communicate online. I have been both a scholar of, and participant in, this panoply of low-carbon digital experiments. In tracing how my thinking has evolved, I seek to provide a self-reflexive assessment of what we might be responding to through these practices and what the role of climate anxiety is or should be in guiding such efforts. While remaining sympathetic to these behavioral shifts, I explore how an emphasis on discrete actions could risk misapprehending the material character of the digital systems we seek to change, overattributing both responsibility and agency to users. I conclude with some evolving criteria for assessing the environmental impacts of digital networks, as well as personal reflections on how the hermeneutics and practices of infrastructural care provides a productive alternative for thinking and action on the issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".