Migration of Mexican Seasonal Farm Workers to Canada and Development: Obstacles to Productive Investment
Bibliographic record
Abstract
This article explores the impact of international labor migration on development in communities of origin. It outlines three theoretical positions corresponding to specific theoretical trends in the field of development. The first position is represented by those who postulate that remittances and acquired skills and knowledge contribute to local development (the optimistic perspective). The second position is represented by those who regard the impact of international migration in predominantly negative terms (the pessimistic perspective). And finally, there are those who believe that some, although limited, growth is possible when transmigrants remit financial and social capital (the moderately optimistic perspective). Based on research on Mexican seasonal workers in Ontario, the article will argue that while international migration can contribute to some economic growth, this growth is limited. While the standards of living of seasonal labor migrants and their households improve (and therefore there is basis for some limited optimism), few among them invest their money in productive activities. Instead, the improvements that the migrants’ households experience are linked to continuous external sources of income. The article illustrates that while Canada-bound migrants experience both structural constraints related to the decline in subsistence agriculture in Mexico and those related to household composition (absence of males from the household), specific criteria used to select participants in the Canadian seasonal farm worker program compound the problems associated with the low potential among these workers to invest remittances productively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".