A Defense for Scientific Realism: Skepticisms, Unobservables & Interference to the Best Explanation
Bibliographic record
Abstract
The epistemological status of scientific knowledge claims has been undermined by skepticism, in particular by universal skepticism. This thesis asserts that Bas C. van Fraassens empirical stance is akin to universal skepticism. This work also maintains that van Fraassens empirical stance does not lead to the conclusion that scientific knowledge claims are empirically adequateespecially those claims that resulted from the scientific method of inference to the best explanation (IBE). To illustrate why van Fraassens stance does not devalue scientific knowledge claims will be suggested via Peter Liptons understanding of IBE combined with Ernan McMullins epistemic values. By bridging McMullins values with Liptons version of IBE, we get a more robust version of IBE; as a result, scientific claims may display a cluster of epistemic virtues and values. Where scientific knowledge claims display a cluster of epistemic virtues and values, they are simply beyond being empirically adequate.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 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".