Bibliographic record
Abstract
In a paper (Mizrahi 2013a) and a reply to critics (Mizrahi 2016a) published in Informal Logic, I argue that arguments from expert opinion are weak arguments. To appeal to expert opinion is to take an expert’s judgment that p is the case as (defeasible) evidence for p. Such appeals to expert opinion are weak, I argue, because the fact that an expert judges that p does not make it significantly more likely that p is true or probable, as evidence from empirical studies on expert performance suggests (Mizrahi 2016a, pp. 246-247). Unlike other critics of this argument (e.g., Seidel 2014 and Walton 2014), who take issue with the empirical evidence on expert performance, David Botting (2018) says that he wants to take issue with the premise that reliability is a necessary condition for the strength of appeals to expert opinion. I respond to Botting’s objections and argue that they miss their intended target. I also argue that his attempt to show that arguments from expert opinion are strong is unsuccessful.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".