The relationship between place of articulation and semantic features in a corpus of astronomical neologisms in Quebec Sign Language
Bibliographic record
Abstract
While the presence of a phonological level is not modality-dependent, modality doeshave an impact on the phonological structure of languages, as illustrated by the significantincorporation of simultaneity into the organization of sign languages compared towhat is found in spoken languages (e.g. Fenlon, Cormier, and Brentari 2017). Modalityalso allows for greater representation of iconicity in sign form(e.g. Östling, Börstell, andCourtaux 2018; Taub 2012). Considering the iconic potential offered by the visuo-spatialmodality of sign languages, this paper aims to answer the following research question:Does semantic motivation, and more precisely iconic motivation, influence the formationof structural components of signs, and specifically, the place of articulation (POA)for the lexical creation of astronomical signs in Quebec Sign Language (LSQ)? We hypothesizedthat, given the semantic domain for which the neologisms were created (i.e.,one that denotes physical/concrete objects, located far from humans), the POA wouldbe distal. Based on a descriptive analysis of the POA sublexical features of 99 neologismsof astronomy in LSQ, we found very little involvements of the POA in the representationof the referent. Although we can explain these results by articulatory-perceptual considerations,we suggest that the semantic domain could also interfere in the creation ofthose neologisms.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".