On Language Distribution in Ilam Province, Iran
Bibliographic record
Abstract
As is still the case in many parts of Iran, the distribution of languages and dialects in Ilam Province, western Iran, is unevenly documented. There have been several studies on specific language varieties spoken throughout the province but, in large part because of conflicting perspectives on the relationship between language and ethnicity, the situation for the region as a whole has until now remained unclear. The present study first of all brings together existing sociolinguistic and demographic data on language distribution and highlight areas of uncertainty. The main part of the study provides an overview of local perceptions of language distribution and language use based on field research and interviews conducted in each of the province's ten regions (shahrestān) and their twenty-five districts (bakhsh). Here, respondents' assessments of the geographic extent of the province's four main languages—Kurdish, Luri, Laki and Arabic—as well as more minor languages spoken by immigrants from elsewhere in Iran are summarized. For Kurdish in particular, which is the major of the four languages, the article shows the perceived geographic distribution of each major dialect and its affiliation within one of two major Kurdish dialect groups: Ilāmi (or “Feyli”) and Kalhōri. This analysis is followed by a brief discussion of multilingualism. The results of the study are brought together in a map of the province's languages.
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 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".