Conrad set out to inform against the family cell. This book is a good start, but there's more to tell yet.
Bibliographic record
Abstract
Basok’s Tortillas and Tomatoes brings to light rich reflections on the problems facing Canadian agriculture, mostly in terms of committed labour shortages during specific time periods of the year. Tomatoes and Tortillas gets points for originality for dealing with Canada and Mexico, two countries that are rarely brought together despite the fact that exchanges of people, capital, and goods continue to expand. These two national realities are presented separately in the book, with the first part dealing more with the problems facing Canadian fresh produce agriculture and greenhouse production, while the second part analyses the migration of Mexican workers, their living conditions in Canada (Leamingford County, Ontario), and Mexican rural poverty. By the end of the book, any loose ends are tied as the author sheds light on the interdependence that has been cultivated between Canadian growers and offshore Caribbean and Mexican labour within the context of the government-led farm workers’ program. Although this interdependence is not explicit in the text, the reader is led to understand how offshore labour programs offer mutual benefits (employment and income in exchange for committed labour in the fields), which allow both migrants and growers to gain while continuing to pursue their own individual interests. Basok’s deep understanding of the conditions met by migrant workers in Canadian fields has been carefully supplemented with an equally deep knowledge of the history of rural development in Mexico and the
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".