Are There Collective or Partially Collective Interpretations of Universal Quantificational “Each” Sentences?
Bibliographic record
Abstract
I intend for this thesis to build from these works.Therefore, I will provide a brief summary of some of the ideas I assume in the background from them.A central idea is that linguistic meanings are language-internal lexical and syntactic cues to language-external thought systems for how to build a "template" for further thought building (see KPT (2023)).This template underlies our preliminary interpretation of a linguistic expression.It provides a conceptual tool that thinkers tacitly use to build a more "complete thought"-to think about particular individuals and events in contexts and to make inferences (ibid., Pietroski ( 2018)).Knowlton and others propose that this "template" interfaces with particular non-linguistic cognitive systems such as the object-file system and "ensemble" representation systems that constrain how we verify sentences in contexts.Below is a schematic representation of Knowlton and others' account of how thinkers interpret the sentence, "each frog is green".
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".