Bibliographic record
Abstract
In a paper published in this journal Martin Hinton aims to show that the struggle between Moti Mizrahi and me about whether arguments from expert opinion are weak arguments rests on misunderstandings (Hinton 2015). Let me emphasize that I generally appreciate Hinton’s intention to settle the dispute between Mizrahi and myself in this way. 1 Furthermore, I also agree with Hinton’s conclusion that if Mizrahi is interpreted in the way Hinton does, then Mizrahi’s “claim becomes far less controversial, but also rather uninteresting” (Hinton 2015, 551)—to refer to the title of my former paper: just spilling out the water wouldn’t be worth a paper in Informal Logic. 2 Let me therefore focus in this reply on the points where Hinton directly attacks my treatment of Mizrahi and also what Hinton takes to be my account of expertise. I will discuss the following criticism of Hinton: (1) that, at points, my attack on Mizrahi is unfair due to my misunderstanding of his intentions, (2) that the notion of expertise I use is self-contradictory/inconsistent, (3) that the argument for my view is circular, (4) that one of my examples—the example from soccer—is mistaken. In rebutting this criticism, I aim to clarify the background of my former paper in this journal.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".