The Sociolinguistic Salience of Linguistic Variables in Najdi Arabic
Bibliographic record
Abstract
This study examines the relative sociolinguistic salience of three linguistic variables in two Najdi dialects, the bedouin and sedentary dialects. The quantitative data elicited from sociolinguistic interviews in Alajmi (2019) shows that bedouins are converging on the sedentary dialect, to varying degrees across the variables. The aim of this study is to test whether the sociolinguistic salience of the variables is the reason why there is variation in the level of convergence. Three methods have been used to measure the relative salience of the variables, the Social Category Association Test (SCAT), dialect identification task and multiple interviewers. The data from all three methods agree with the level of convergence in the production data (Alajmi, 2019). The variable that shows high level of convergence to the sedentary variant was found to be salient, while the other variables which show low levels of convergence were not salient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".