Bibliographic record
Abstract
This paper offers a defense of scientific realism against one central anti-realist argument, the pessimistic meta-induction (PMI). More specifically, this paper initially considers and rejects an oft-stated version of the PMI, arguing that the historical sample size is insufficient to make any serious induction, optimistic or pessimistic, about the likelihood of current scientific theories being abandoned. After demonstrating the deficiency of the initially considered PMI, the paper takes into account a possible amendment to the PMI which could circumvent such sample-size worries, but then concludes that even this amended version of the PMI does not offer sufficient warrant for abandoning scientific realism. Before diving headlong into these arguments against the PMI, it will be helpful to review the general realist and antirealist positions that stake out the terms of the debate.
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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 | 0.046 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".