Bibliographic record
Abstract
Abstract: Yu and Zenker (2022) argue that the oft-made distinction between convergent and linked argument structure is problematic. If their account holds, the linked/convergent distinction, at least as I have characterized it (Freeman 2011), seems to violate the dictum that structural analysis should come before evaluation. In this Reply I defend the position that we do not need to estimate or determine argument strength to determine whether the premises of an argument are linked or convergent. Résumé: Yu et Zenker (2022) soutiennent que la distinction souvent faite entre structure d’argumentation convergente et structure d’argumentation liée est problématique. Si leur explication est valable, la distinction liée/convergente, du moins telle que je l’ai caractérisée (Freeman 2011), semble violer le dicton selon lequel l’analyse structurelle doit précéder l’évaluation. Dans cette réponse, je défends la position selon laquelle nous n’avons pas besoin d’estimer ou de déterminer la force de l’argument pour déterminer si les prémisses d’un argument sont liées ou convergentes.
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.037 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".