Bibliographic record
Abstract
Abstract: The scholarship on deep disagreements presents us with a considerable number of seemingly disparate characterizations concerning the nature of these disputes. This paper is motivated by the desire to grasp what these characterizations are. An answer is provided through the method of reconstructive analysis. Two ideal and paradigmatic models of deep disagreements are defined initially. Then, individual characterizations found in the scholarship are examined against the background of such models. Special attention is given to Fogelin’s paper, the work that initiates modern discussion on deep disagreements. According to the interpretation provided in the following paper, both models inadvertently coexist in this seminal work. Résumé: Les études sur les désaccords profonds nous présentent un nombre considérable de propriétés apparemment disparates concernant la nature de ces conflits. La rédaction de cet article est motivée par le désir de comprendre quelles sont ces propriétés. Une réponse est fournie par la méthode de l'analyse reconstructive. Deux modèles idéaux et paradigmatiques des désaccords profonds sont d'abord définis. Ensuite, les propriétés individuelles trouvées dans les études sont examinées dans le contexte de ces modèles. Une attention particulière est accordée à l'article de Fogelin, l'ouvrage qui initie la discussion moderne sur les désaccords profonds. Selon l'interprétation fournie dans l'article suivant, les deux modèles coexistent par inadvertance dans cet ouvrage fondateur.
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 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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".