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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".