Conceptual Relations Predict Colexification across Languages
Bibliographic record
Abstract
In natural language, multiple meanings often share a single word form, a phenomenon known as colexification. Some sets of meanings are more frequently colexified across languages than others, but the source of this variation is not well understood. We propose that cross-linguistic variation in colexification frequency is non-arbitrary and reflects a general principle of cognitive economy: More commonly colexified meanings across languages are those that require less cognitive effort to relate. To evaluate our proposal, we examine patterns of colexification of varying frequency from about 250 languages. We predict these colexification data based on independent measures of conceptual relatedness drawn from large-scale psychological and linguistic resources. Our results show that meanings that are more frequently colexified across these languages tend to be more strongly associated by speakers of English, suggesting that conceptual associativity provides an important constraint on the development of the lexicon. Our work extends research on polysemy and the evolution of word meanings by grounding cross-linguistic regularities in colexification in basic principles of human cognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".