Bibliographic record
Abstract
Like the nameless character in Kevin Jared Hosein’s Commonwealth award-winning story “Passage,” we are not going to make it out with our bodies intact and we have come too far to turn round from the necessity of engaging the complex implications that arise between the weather and our bodies. “Climate crisis” is one such implication that this article explores. It turns to strange weather events narrated in Hosein’s story and in news media. Taking guidance from Hosein’s narrative and reading news narratives through an interpretive disability studies framework, the article reveals the tangled relation between conceptions of the body and the environment that allows for second thoughts on what scientifically based climate crisis discourse is not providing. This reading reveals that the ways of knowing crisis need a critical, imaginative reworking. The analysis suggests that by grappling with the interpretive complexity that is now our strange weather can we better understand and less easily perpetuate the crisis that we have produced. Such grappling requires attending to how bodies (dis)appear in weather narratives produced by bureaucratic-scientific rationality.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".