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 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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.001 |
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 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".