Bibliographic record
Abstract
The paper connects affect studies with Indigenous Studies, Science and Technology Studies, and the emergent field called extinction studies or climate change studies. Claire Colebrook’s two 2014 Deleuzean books on extinction argue for a “theory beyond theory,” where affect would have no place: theory would think beyond human extinction. The paper examines two important categories of discourse around the polar bear, that poster creature for climate change, those of Inuit hunters and elders and those of scientists. The Inuit freely express emotions, the scientists do not. The Inuit see themselves and the polar bear as kin; the scientists’ concern for the bears is not articulated. Nonetheless, for both of them, the bears are what the science and technology studies scholar, Bruno Latour, calls a “matter of concern.” Non-Inuit artistic responses to the possible extinction of the polar bear reveal a strong affective response, unlike the scientific accounts. Perhaps Latour’s suggestion for a “parliament of things” where non-human entities that have become “matter of concern” are represented might help connect these disparate discourses. Although Latour may be too optimistic, Colebrook’s stance seems require an impossible denial of human affect. Even the rational scientific accounts evidence concern based on affect, however buried. One approach that seems useful is that of Theo van Dooren, who looks at how threatened species and humans are connected in an account that examines affect as part of a study that also draws on science.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".