Toward a picture of Chahar Mahal va Bakhtiari Province, Iran, as a linguistic area
Bibliographic record
Abstract
Abstract Language documentation has been carried out in Iran since the late 1800s but in a sporadic way, and even now, the scholarly picture of the country’s linguistic landscape is fragmentary. The present article responds to this state of affairs in a modest way by working toward a systematic overview of the language situation in one area of the country: Chahar Mahal va Bakhtiari Province of western Iran, where the high Zagros Mountains open onto the Iranian Plateau. In this study, conducted in the context of theAtlas of the Languages of Iran(ALI) research programme, we chronicle our research process for this region, beginning with an inventory of languages spoken here—varieties of Bakhtiari, Charmahali, and Turkic—and an overview of their geographical distribution. This initial step enabled us to select 30 varieties from 26 locations across the province for in-depth research, including implementation of the ALI language data questionnaire. Data generated by the study have resulted in two language distribution maps as well as a series of linguistic structure maps. Initial analysis of lexical and phonological data provides insight into defining features of each language as well as structures shared between them as a result of language contact in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".