Bibliographic record
Abstract
Many sign languages have semantically related noun-verb pairs, such as ‘hairbrush/brush-hair’, which are similar in form due to iconicity. Researchers studying this phenomenon in sign languages have found that the two are distinguished by subtle differences, for example, in type of movement. Here we investigate two young sign languages, Israeli Sign Language (ISL) and Al-Sayyid Bedouin Sign Language (ABSL), to determine whether they have developed a reliable distinction in the formation of noun-verb pairs, despite their youth, and, if so, how. These two young language communities differ from each other in terms of heterogeneity within the community, contact with other languages, and size of population. Using methodology we developed for cross-linguistic comparison, we identify reliable formational distinctions between nouns and related verbs in ISL, but not in ABSL, although early tendencies can be discerned. Our results show that a formal distinction in noun-verb pairs in sign languages is not necessarily present from the beginning, but may develop gradually instead. Taken together with comparative analyses of other linguistic phenomena, the results lend support to the hypothesis that certain social factors such as population size, domains of use, and heterogeneity/homogeneity of the community play a role in the emergence of grammar.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".