An Ecolinguistics Analysis on Lexicalization of Domestic Animals in Kurmanji Kurdish
Bibliographic record
Abstract
Kurmanji is one of the three main varieties of Kurdish language, scattered across some areas of Turkey, Syria, Iraq, and both the West and Northeast parts of Iran. The current study adopted the taxonomy organization approach in analyzing the lexicon related to conceptualization of domestic animals in Kurmanji according to Ecolinguistic frame. The data collection method follows the Guide to collecting dialects for the treasury of Iranian dialects. It operates through three stages: literature study, individual interview, and focus group interview. Data was analyzed based on lexican process suggested by Malt, Sloman, & Gennari and, the analyzing frame of folk biological classification and nomenclature, presented by Berlin, Breedlove, & Raven. Participants were selected based on convenience sampling, and the interview process continued till the saturation point was reached. Participants were native speakers aged 45-75 years old. Results revealed that “Same porotypes, different boundaries”, and “Nesting” imply a significant role in Kurmanji nomenclature of domestic animals and led to a recognizable classification of the lexicon based on factors like gender, age, color, verbs, treatment, and occupation. The Kurmanji culture seems to be enriched by the Folkbiological knowledge of speakers and their direct experiences. Also, cultural factors such as the experience of direct exposure and extensive ecological knowledge regarding domestic animals have led to the lexicalization of various concepts to illustrate the magnitude value of animals in Kurmanj’s culture and lifestyle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".