LETTER TO THE EDITORS: SOLVING VINCENT CARRET’S PUZZLE: A REBUTTAL OF CARRET’S FALLACIES AND ERRORS
Bibliographic record
Abstract
In his article published in JHET in 2022, Vincent Carret (2022a) criticizes our work. In footnote 19, pages 630–631, he claims that our result “is based on a mistaken interpretation of the paragraph at the bottom of p. 191 of Frisch (1933).” He then states that we “take to mean that the coefficient of each cycle in the general sum of solutions is arbitrary, while … these coefficients [depended] on initial conditions and the parameters of the system.” The present rejoinder aims at rebutting Carret’s allegation of mistaken interpretation in our work. We demonstrate that his statements are based on a misunderstanding of Frisch’s econometric model and approach. Then, we show that Carret’s results are not supported by the demonstration he claims to have made, and that he misrepresents our arguments.
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.050 | 0.048 |
| Insufficient payload (model declined to judge) | 0.005 | 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".