Bibliographic record
Abstract
How we can enact meaningful change in computing to meet the urgent need for sustainability and justice. The deep entanglement of information technology with our societies has raised hope for a transition to more sustainable and just communities—those that phase out fossil fuels, distribute public goods fairly, allow free access to information, and waste less. In principle, computing should be able to help. But in practice, we live in a world in which opaque algorithms steer us toward misinformation and unsustainable consumerism. Insolvent shows why computing's dominant frame of thinking is conceptually insufficient to address our current challenges, and why computing continues to incur societal debts it cannot pay back. Christoph Becker shows how we can reorient design perspectives in computer science to better align with the values of sustainability and justice. Beckerpositions the role of information technology and computing in environmental sustainability, social justice, and the intersection of the two, and explains why designing IT for just sustainability is both technically and ethically challenging. Becker goes on to argue that computing could be aided by critical friends—disciplines that draw on critical social theory, feminist thought, and systems thinking—to make better sense of its role in society. Finally, Becker demonstrates that it is possible to fuse critical perspectives with work in computer science, showing new and fruitful directions for computing professionals and researchers to pursue.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.242 | 0.145 |
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".