Lexicons encode differently what people do differently. Computational studies of the pragmatic motivations of lexical typology.
Bibliographic record
Abstract
Languages differ in what meanings their lexical items encode: The meaning covered by English 'blue' is famously split into 'sinij' (darkblue) and 'goluboj' (lightblue) in Russian. Recent years have seen novel interest in functional explanations of such variation, pointing to a correlation between greater communicative need of a lexical field and a finer-grained lexical inventory. Here, I develop the position that rather than the mere difference in “need” to mention lexical field, it is the field's discourse-pragmatic diversity that predicts whether languages “lump” or “split” more. I will demonstrate this with computational techniques and a typologically diverse corpus of spontaneous spoken data from 51 languages (DoReCo), first for the field of verbs of visual perception ('see'-'look'), then on a lexicon-wide level. There are implications: our notions of what a comparable concept is in lexical semantics, what lexical knowledge entails, and the dimensions along which languages differ require re-examining.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".