Subatomic and plural homogeneity as exhaustification effects of different kinds
Bibliographic record
Abstract
Homogeneity (all-or-nothing) effects are observed in both atoms and pluralities. In this paper, I compare two theories of homogeneity. The first is made for plural homogeneity and the second for subatomic homogeneity, but both capture the effect through existential lexical meaning paired with local exhaustification in positive sentences. I show that neither approach can be extended to the side of the paradigm it was not intended for. But relying on exhaustification for at least subatomic homogeneity is well motivated: all predicates belonging to taxonomies (rather than scales), whether displaying homogeneity effects or not, are interpreted as weak/strong in the same environments, and it is not clear what mechanism other than exhaustification could account for these facts in a united way. Hence, I maintain that homogeneity is an exhaustification effect, and suggest that plural and subatomic homogeneity are simply due to different kinds of local exhaustification.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".