Bibliographic record
Abstract
Clear criteria for the identity of dispositions are still lacking, and this has been presented as one of the main challenge raised by such entities. It is of prime importance to identify or distinguish dispositions such as diseases or risks. This article first introduces conventional ways to refer to a disposition (such as “fragility”) and canonical ways (such as “disposition to break in case of a strong shock”). This raises the issue of how should exactly be defined a “disposition d to R when TR”, where R is a realization specification and TR a trigger specification. Two ontological frameworks are distinguished. The first framework, which has been largely used so far in the literature on dispositions, interprets d as a disposition which can only be triggered by instances of TR, and can only be realized by instances of R. The second, new framework introduces the notion of “minimal trigger” and “maximal realization”, and interprets TR as a parent class of a class of processes that have as part a minimal trigger, and R as a parent class of a class of processes that are parts of a maximal realization. We then discuss several criteria of identity, including the criterion according to which two dispositions are identical iff they have the same categorical basis, the same class of minimal triggers and the same class of maximal realizations. We show on several examples that the second framework avoids the disposition multiplicativism that is introduced by the first framework.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".