Communities of practice and lexical variation in the Montréal Turkish community
Bibliographic record
Abstract
This study examines the social organization of the Turkish community in Montréal and its influence on language use. The Montréal Turkish community has been growing since the 1960s as a result of various waves of migration. Bilge (2004) explained the fragmented structure of the community through ethnicity (Turks, Kurds and Armenians). However, conservative movements have grown stronger in the last two decades in Turkey and recent socio-politic changes are mostly based on religion rather than ethnicity. I anticipate that these sociological changes in Turkey have an impact on the organization of the Turkish community in Montréal and that I can observe the social identity of the members of the Turkish community in Montréal through lexical variation. To verify this prediction, I used a dual methodology: participant observation and analysis of the words used by participants to describe the structure of the Montréal Turkish community, the group to which they feel they belong, and other groups. The ethnographic study confirms that conflicts triggered by the socio-political structure and national ideology in the country of origin are determining factors in the organization of the Montréal Turkish community. Montréal Turks form an immigrant community divided into at least two communities of practice, traditionalist and progressive, each with its own socialization sites and its own discourse/style.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".