Are There Collective or Partially Collective Interpretations of Universal Quantificational “Each” Sentences?
Bibliographic record
Abstract
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".Syntactic object in Language faculty Template building in thought x:Frog(x)[Green(x)] (instructions sent to thought) Verification procedures that ensue from the template Object-file 1: frog 1 Object-file 2: frog 2 Object-file 3: frog 3 (Based on KPT (2022, 2023))Knowlton and others concluded from a number of psychosemantic experiments that the meanings of "each"-sentences call for building a template in thought that has a logical structure such as the first-order equation above.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".