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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".