Bibliographic record
Abstract
In this commentary, I respond to Ruez and Cockayne’s ‘Feeling Otherwise’ in a moment of intense ‘otherwise-ness’ as a global pandemic upends daily life in a variety of mundane and profound ways. Provoked by Ruez and Cockayne to take up the idea of the stories we tell, I reflect on ambivalence and writing into a world deeply undecided. Although it is not hard to detect accounts of this crisis at both the ‘paranoid’ and affirmative ends of an affective spectrum, there is also perhaps an unprecedented ambivalence seeping into our stories, one which holds potential for disrupting some of our taken-for-granted ideas about how the world works. As we attempt to use stories to make sense of this changing world and to write into being a world we want to live in, we must, as Ruez and Cockayne insist, remain attentive to difference and resist the pull of a universal, masterful story. I suggest getting comfortable—or staying uncomfortable—in the queasy, sweaty space of undecidability.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".