The nature and norms of scientific explanation: Some preliminaries
Bibliographic record
Abstract
There are at least two deep and related debates about explanation: about its nature and about its norms. The aim of this special issue of Philosophical Problems in Science/Zagadnienia Filozoficzne w Nauce (ZFN) is to survey whether or not a consensus is at hand in these debates and to help settle what it can. The overarching foci are twofold: (i) the nature of scientific explanation, with special attention to the debate between ontic and epistemic conception of explanation, and (ii) the norms of scientific explanation, with special attention to so-called ‘ontic’ (or better, ‘alethic’) norms like truth and referential success and epistemic norms like intelligibility and idealized understanding. It called for advocates of various conceptions to articulate the current state of these debates. Researchers and scholars from around the globe—including Poland, Canada, Korea, The Netherlands, the United States, Greece, Austria, and Belgium—contributed. The special issue also attempts to provide an opening for new work on the norms of explanation, such as truth or model-based accuracy, information compression, abstraction, and generalization.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".