Toward a picture of Chahar Mahal va Bakhtiari Province, Iran, as a linguistic area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 the Atlas 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.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it