Bibliographic record
Abstract
ABSTRACT In the philosophy of logic, the Adoption Problem is a challenge to the claim that reasoners can, in certain ways, rationally change which logic they use. The (alleged) problem is that if someone does not already infer in accordance with some fundamental logical principles (such as Universal Instantiation or Modus Ponens), then they cannot rationally begin to do so: the “adoption” of these principles is either unnecessary or impossible. In the literature, three issues have emerged as especially contentious: (1) How should we understand the argument for the Adoption Problem? What exactly is the argument's conclusion, and how is it established, if at all? (2) How could someone who thinks that the rational adoption of logic is possible respond to the Adoption Problem? (3) What are the consequences of the Adoption Problem for related issues in the philosophy of logic? In this paper, we address each question in turn. We suggest that the Adoption Problem is best understood in the form of an inconsistent quartet of theses regarding logical inference. We classify positions on logical adoption in terms of which of these theses is abandoned, and we show that such a taxonomy of positions is useful for delineating the scope and consequences of the Adoption Problem.
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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 | 0.032 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".