Media Review: Extrapolations – A View from OS4F
Bibliographic record
Abstract
As we draw inexorably closer to a two – and, more likely to a three – degree Celsius rise in global temperatures above pre-industrial levels, we, as members of OS4Future[i] feel increasingly compelled to raise awareness of the reality of the climate crisis. This topic has only recently gained more widespread attention by management researchers, drawing attention to questions of climate justice, problematizing short-term market incentives and the pressures for business as usual, or proposing prefigurative and more sustainable forms of organizing. Nonetheless, organization scholars, with few exceptions (e.g., Dentoni, 2024), still do not seriously entertain the possibility of severe disruption. This is a collective failure of imagination that requires different modalities besides scientific reports in order to convey the urgency of the situation.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.018 | 0.039 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".