Lexicons encode differently what people do differently. Computational studies of the pragmatic motivations of lexical typology.
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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