The arbitrariness of the sign: Learning advantages from the structure of the vocabulary.
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
Recent research has demonstrated that systematic mappings between phonological word forms and their meanings can facilitate language learning (e.g., in the form of sound symbolism or cues to grammatical categories). Yet, paradoxically from a learning viewpoint, most words have an arbitrary form-meaning mapping. We hypothesized that this paradox may reflect a division of labor between 2 different language learning functions: arbitrariness facilitates learning specific word meanings and systematicity facilitates learning to group words into categories. In a series of computational investigations and artificial language learning studies, we varied the extent to which the language was arbitrary or systematic. For both the simulations and the behavioral studies, we found that the optimal structure of the vocabulary for learning incorporated this division of labor. Corpus analyses of English and French indicate that these predicted patterns are also found in natural languages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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