Bibliographic record
Abstract
Abstract: I am most grateful to the editors of Informal Logic for their willingness to publish my absurdly long paper (Siegel 2023a) in its entirety, and for organizing the four commentaries published along with it. I am grateful as well to Bart Garssen, Andrew Aberdein, Paula Olmos and Christoph Lumer for their insightful and challenging discussions. In what follows I respond to their criticisms and suggestions in the order in which they appear in the journal. Résumé: Je suis très reconnaissant aux éditeurs d’Informal Logic pour leur volonté de publier mon article absurdement long (Siegel 2023a) dans son intégralité et pour avoir organisé les quatre commentaires publiés avec lui. Je suis également reconnaissant à Bart Garssen, Andrew Aberdein, Paula Olmos et Christoph Lumer pour leurs discussions perspicaces et stimulantes. Dans ce qui suit, je réponds à leurs critiques et suggestions dans l’ordre dans lequel elles apparaissent dans la revue.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".