Bibliographic record
Abstract
From its inception in 1966, the Canadian Seasonal Agricultural Worker Program (SAWP) has grown to employ approximately 20,000 workers annually, the majority from Mexico. The program has been hailed as a model that alleviates human rights concerns because, under contract, SAWP workers travel legally, receive health benefits, contribute to pensions, are represented by Canadian consular officials, and rate the program favorably. Tomorrow We're All Going to the Harvest takes us behind the ideology and examines the daily lives of SAWP workers from Tlaxcala, Mexico (one of the leading sending states), observing the great personal and family price paid in order to experience a temporary rise in a standard of living. The book also observes the disparities of a gutted Mexican countryside versus the flourishing agriculture in Canada, where farm labor demand remains high. Drawn from extensive surveys and nearly two hundred interviews, ethnographic work in Ontario (destination of over 77 percent of migrants in the author's sample), and quantitative data, this is much more than a case study; it situates the Tlaxcala-Canada exchange within the broader issues of migration, economics, and cultural currents. Bringing to light the historical genesis of "complementary" labor markets and the contradictory positioning of Mexican government representatives, Leigh Binford also explores the language barriers and nonexistent worker networks in Canada, as well as the physical realities of the work itself, making this book a complete portrait of a provocative segment of migrant labor.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".