La formación de capital humano en el Estado de México: un análisis logístico de las remesas provenientes de Canadá
Bibliographic record
Abstract
The economic expression of migrations, particularly international ones, is remittances. In general, these are meant to cover the basic needs of consumption, however the participants in the Canadian Seasonal Agricultural Program (CSAWP Mexico-Canada) allocate them among other things, to the education of the children and the family of the migrant. The main objective of this paper is to know, based on a logit model and on the theory of human capital2 , whether participating in this Program helps the formation of human capital of Mexican immigrants in the PTAT and their families. The results indicate that being part in the CSAWP contributes to human capital formation in form the migrant and his/her relatives, since remittances – along with the accumulation of knowledge and experiences - increase the propensity to invest in formal education and also migrants can replicate the knowledge acquired in Canada for his agricultural work in Mexico. The variables that positively impact the formation of capital are the permanence and duration of the migrant in the program (between 7 and 12 uninterrupted years) and the one that groups the net income that ranges from CAD $ 10,000 to CAD $ 14,000. However, those who are over 43 years of age, those who previously migrated to the USA and those who come from the southern part of the state of Mexico are not human capital trainers. The database was obtained through a survey applied in 2011 to 67 migrants participating in the program in the state of Mexico: 64 men and three women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".