Bibliographic record
Abstract
Argumentation theorists know that their work has real-life application, and similarly, they draw inspiration for that work from real-life experiences. Sometimes, it comes from some public medium – the newspaper, a blog, a debate stage. But we also draw from more private reason-exchanges – a conversation with a neighbor, small-talk with a colleague, or a lovers’ spat. A few worries about publicly theorizing about those more private cases arise. We may be making public something that was unguarded, and so betray a trust. Our theoretical reflections may themselves warp the relationship we’d originally savored, particularly when our partners know about the possibility of them being publicly scrutinized. Novelists and poets regularly struggle with this challenge with their work, and we argumentation theorists should, too. Les théoriciens de l’argumentation savent que leur travail a des applications concrètes et, de la même manière, ils s’inspirent d’expériences réelles. Parfois, ces idées proviennent d’un média public – un journal, un blog, une tribune de débat. Mais nous nous inspirons aussi d’échanges de raisonnement plus privés – une conversation avec un voisin, une petite conversation avec un collègue ou une dispute amoureuse. Nous craignons de théoriser publiquement sur ces cas plus privés. Nous pouvons rendre public des commentaires irréfléchis et ainsi trahir une confiance. Nos réflexions théoriques peuvent déformer la relation que nous avions initialement très appréciée, en particulier lorsque nos partenaires savent qu’ils peuvent être scrutés publiquement. Les romanciers et les poètes sont régulièrement confrontés à ce défi dans leur travail, et nous, les théoriciens de l’argumentation, devrions également le faire.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.101 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".