Measuring lexical and structural conventionalization in young sign languages
Bibliographic record
Abstract
Abstract Compounding, as a nearly universal word-formation process that is very useful in emerging languages, might be expected to conventionalize early in a language’s history. However, a recent study focusing on novel compounding in ISL and ABSL found that this may not be the case, and moreover, that the two languages appear to differ in how compounding is conventionalizing ( Tkachman & Meir 2018 ). In this paper, we follow up on their findings, using six new measures to further evaluate lexical and structural conventionalization in the same set of novel compounds elicited by Tkachman & Meir (2018) . We found that ISL shows more lexical convergence, whereas ABSL shows more structural convergence. We propose that the differences in conventionalization we observe can be linked to the different social circumstances of these languages ( Meir et al. 2010 ).
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".