Bibliographic record
Abstract
Abstract Those concerned with climate governance will want to keep watching what is happening in AI governance. Far from unrelated, the two parallel one another in terms of how fractions of capital—whether within fossil fuel or tech sectors—call for legislating in the face of crisis or for voluntary pledges. In truth, both may be said to be forms of self‐governance. Climate and AI intersect firstly in how they are imagined: dominant climate and AI discourses are both symptoms of Anthropocene thinking and ‘capitalist realism’. They also intersect in as much as ‘AI for Good’ initiatives propose that AI is ethical because it can help to address climate change. What seems missing, however, is any consideration of this climate AI as a procedure—is its knowledge valid, what knowledges does it displace or exclude, what biases are reproduced?—and consideration for its consequences, including harms. Does it actually result in climate mitigation and/or adaptation in a given context? What ‘maladaptive’ outcomes might it drive? What alternatives does it foreclose? These sorts of questions are ones where geographers will continue to have a lot to say.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".