Exceeding Our Grasp: Science, History, and the Problem of Unconceived Alternatives, by P. Kyle Stanford.
Bibliographic record
Abstract
The debate between scientific realists and anti-realists goes on, each side drawing new arguments from a seemingly bottomless reservoir, only to have them repudiated by the opposing party. A little while ago it was structural realism. Now Kyle Stanford presents us with a new twist on one of the classical arguments for anti-realism: the pessimistic induction. The old argument is simple. Its premiss is that past scientific theories have always turned out to be false; therefore, by induction, we must expect that our current and future theories will also turn out to be false — and therefore the anti-realists are right to enjoin us not to believe any theories. As for Stanford’s new argument, I have a pessimistic induction of my own: all past arguments in support of either realism or anti-realism have been found to be defective; therefore I predict that present and future arguments for realism or anti-realism will also turn out to be defective. Let us see whether my prediction is confirmed in this case.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".