Bibliographic record
Abstract
The ad hominem attack is the negative use of personal argumentation to undermine or destroy the credibility of a person in a critical discussion. An opposite type of tactic is the argument from expert opinion, which uses the opinion of a respected authority or expert on a subject as positive personal argumentation to support one's own side of an argument. The ad hominem criticism attacks a person as an untrustworthy source, while the argument from expert opinion cites an expert who is presumably reliable and authoritative as a source of advice. In certain respects however, these two types of argumentation are similar. Both are appeals to personal sources of opinion that center on the internal position or credibility of a particular individual as a reliable source of knowledge. Both types of argumentation can be contrasted with the appeal to external or objective knowledge, which comes from scientific evidence such as experimental observations, the kind of knowledge that comes from nature, not from a personal source. In general, the use of argument from expert opinion is a reasonable, if inherently defeasible, type of argument. Appeals to expert opinion can be a legitimate form of obtaining advice or guidance for drawing tentative conclusions on an issue or problem where objective knowledge is unavailable or inconclusive. It is well recognized in law, for example, where expert testimony is treated as an important kind of evidence in a trial, even though it often leads to conflicting testimony, in a “battle of the experts.”
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".