Why does polysemy vary across languages? An explanation in the framework of the Sign Theory of Language / Pourquoi la polysémie varie-t-elle d'une langue à l'autre? Une explication dans le cadre de la Théorie du langage basée sur le signe
Bibliographic record
Abstract
Abstract Many pairs of words traditionally treated as crosslinguistic equivalents do not share the same set of senses, and dominant theories fail to account for this asymmetry. This article proposes an explanation for the crosslinguistic variation of polysemy based on two key insights from Bouchard's Sign Theory of Language. First, multifunctional words have only a single, abstract meaning, and second, properties of the linguistic sign follow from properties of the external systems with which language interfaces. The article describes the content of the English and French deictic verbs go, aller, come, and venir, showing that each possesses a simple semantic representation composed of primitives from general cognition. It then examines several specific semantic uses of go and aller, showing that differences in the surface polysemy of these verbs follow directly from a single difference in their abstract lexical meaning and the way the latter interacts with context, extralinguistic knowledge and grammar.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".