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
Recently, researchers interested in the nature and origins of semantic representations have investigated an especially informative case study: The acquisition of the word most—a quantifier which by all accounts demands a sophisticated second-order logic, and which therefore poses an interesting challenge to theories of language acquisition. According to some reports, children acquire most as early as three years of age, suggesting that it does not draw on cardinal representations of quantity (contrary to some formal accounts), since adult-like knowledge of counting emerges later in development. Other studies, however, have provided evidence that children acquire most much later—possibly by the age of 6 or 7—thereby drawing this logic into question. Here we explore this issue by conducting a series of experiments that probed children’s knowledge of most in different ways. We conclude that children do not acquire an adult-like meaning for most until very late in development—around the age of 6—and that certain behaviors which appear consistent with earlier knowledge are better explained by children’s well-attested bias to select larger sets (a “more” bias), especially when tested with unfamiliar words.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it