Mexican Temporary Agricultural Workers in Canada: a Language and Migration Approach
Bibliographic record
Abstract
The purpose of this study is to fill a gap in the sociolinguistic research on language issues faced by temporary migrants. My research involves a compilation and analysis of the sociolinguistic facts relating to the situation of Mexican Temporary Agricultural Workers (MTAW) who come to Ontario and Quebec through the Seasonal Agricultural Workers Program (SAWP). Following an ethnographic approach and methodology, I investigated the following research questions: 1). How do the biographic backgrounds –human capital- of MTAW restrict or allow them to renegotiate their identity and to be able to deal with their new social and linguistic environment? 2). What and how are the communicative practices of MTAW? 3). What linguistic barriers do MTAW face and how does it affect their daily lives? 4). How do the receiving communities include or exclude MTAW? Among other results, I have found that MTAW live in conditions where language/dialect and contacts happen. However, MTAW’s communicative practices show a stable language maintenance phenomenon, with transidiomatic[1] practices (Jacquemet, 2005), where sociolinguistics barriers impact their lives in almost every space of their life creating dehumanizing barriers that marks them as vulnerable individuals that suffer from linguistic inequalities and exclusion. On the other hand, these same conditions have promoted social awareness among the community at different levels, where there has been an active participation to help MTAW adapt to the community, while at the same time the community also tries to adapt to MTAW’s seasonal presence and needs.\n[1] Transidiomatic practices describe communicative practices of transnational groups with linguistic interactions using different languages and codes (Jacquemet, 2005).
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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 | 0.006 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".