Bibliographic record
Abstract
According to the hypothesis-generator account, valid extrapolations from a source to a target system are circular, since they rely on knowledge of relevant similarities and differences that can only be obtained by investigating the target, thus removing the need to extrapolate; hence, extrapolative reasoning can only be useful as a method for generating hypotheses. I reject this view in favour of an inferential account, focused on extrapolations underpinning the aggregation of experimental results, and explore two lines of argumentation supporting the conclusion that these extrapolations can be validated in a noncircular manner. The first argument relies on formal proofs of inferential validity demonstrating that it is possible to reason from prior knowledge of causal structures in order to determine whether a claim can be extrapolated. The second argument builds on the fact that the hypothesis-generator account overlooks key inferential and experimental practices resulting in progressively better-informed extrapolations.
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 | 0.048 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.007 | 0.030 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".