Semantic Typology and Efficient Communication
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
Crosslinguistic research on domains including kinship, color, folk biology, number, and spatial relations has documented the different ways in which languages carve up the world into named categories. Although word meanings vary widely across languages, unrelated languages often have words with similar or identical meanings, and many logically possible meanings are never observed. We review research suggesting that this pattern of constrained variation is explained in part by the need for words to support efficient communication. This research includes several recent studies that have formalized efficient communication in computational terms and a larger set of studies, both classic and recent, that do not explicitly appeal to efficient communication but are nevertheless consistent with this notion. The efficient communication framework has implications for the relationship between language and culture and for theories of language change, and we draw out some of these connections.
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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 | 0.000 | 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