Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is not surprising to find that the quantity picked out by terms like a few and a lot is context dependent.We can easily accept that a few books might be 10 books, yet a lot of smartphones might only be 4 smartphones.The current paper posits that there are two hypotheses that can explain can explain this context dependency: the Definite Number Hypothesis (DNH), and the Gradable Quantifier Hypothesis (GQH).The DNH suggests that the term a few corresponds to a definite range of values, and may pick out a larger quantity only if the range seems implausible for the given context.The GQH suggests that context-dependency is actually built into the meaning of a few.Experiment 1 supports the intuition that there is variability in the quantity that a few picks out based on context.The findings of Experiments 2 and 3 support the Gradable Quantifier Hypothesis.
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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 | 0.003 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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 it