Bibliographic record
Abstract
Error is a common part of scientific practice, which must be accounted for by scientonomy. A scientific error occurs when an agent accepts a theory that should not have been accepted given that agent’s employed method. One might suspect that the handling of scientific error seems to violate the theory rejection theorem according to which a theory becomes rejected only when other theories that are incompatible with the theory become accepted, because it appears as though a theory isn’t replaced by anything. Here, we analyze several instances of scientific error and show that error handling, when properly analyzed, is fully consistent with the theory rejection theorem. We show that instances of scientific error typically involve the rejection of an erroneous conclusion as well as one or more of the premises of the argument that leads to that erroneous conclusion. In most cases, first-order propositions of the original erroneously accepted theory are replaced by other first-order propositions incompatible with them. In some cases, however, first-order propositions are replaced by second-order propositions asserting the lack of sufficient reason for accepting these first-order propositions. In both cases, such a replacement is fully consistent with the theory rejection theorem.
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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 | 0.063 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| 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".