Bibliographic record
Abstract
Abstract: How do we know that we are in a deep disagreement – i.e. disagreement irresolvable by rational means? Some suggest, we know that only after trying out all our rational arguments. However, such strategy risks backfiring by polarizing the parties. This paper proposes an alternative way of recognizing depth. Drawing on epistemic capacity of emotions, I argue that debater’s emotional experience during deep disagreement, namely frustration, functions as an indicator of the disagreement’s depth. Résumé: Comment savons-nous que nous sommes en profond désaccord, c'est-à-dire insoluble par des moyens rationnels ? Certains suggèrent que nous ne le savons qu'après avoir présenté tous nos arguments rationnels. Cependant, une telle stratégie risque de se retourner contre nous en polarisant les parties. Cet article propose une autre façon de reconnaître la profondeur du désaccord. En m'appuyant sur la capacité épistémique des émotions, je soutiens que l'expérience émotionnelle du débatteur lors d'un profond désaccord, à savoir la frustration, fonctionne comme un indicateur de la profondeur du désaccord.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".