Bibliographic record
Abstract
Inventory: what realists know Entity realism (ER) and epistemic structural realism (epistemic SR) are proposals for realist humility. They offer to distinguish parts of scientific theories that are good bets for knowledge from others that are less so, thus allowing the realist to come to grips with the fact that accepted theories change over time. Neither ER nor epistemic SR, however, is humble in quite the right way. Their prescriptions for how realists ought to be selective sceptics are problematic and ultimately, I believe, untenable. As steps in the evolution of realism, however, they are on the right track, and I have aimed to incorporate the best insights of both under the heading of ‘semirealism’. The lesson of ER concerns the epistemic basis of claims about unobservables. By emphasizing causation, ER captures the common and deeply held realist intuition that the greater the extent to which one seems able to interact with something – at best, manipulating it so as to bring about desired outcomes – the greater the warrant for one's belief in it. But ER attempts to separate a knowledge of entities from a knowledge of their relations, and this cannot be done. It also gives encouragement to awkward diagnoses of historical events. Imagine a review of the evidence considered by different physicists, over time, for thinking they had detected a negative charge.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".