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Record W3018791174

La formación de capital humano en el Estado de México: un análisis logístico de las remesas provenientes de Canadá

2020· article· es· W3018791174 on OpenAlexaboutno aff
Gavilanes Carvajal, Oswaldo García, Raúl de Jesús Gutiérrez

Bibliographic record

VenueRILCO DS: Revista de Desarrollo sustentable, Negocios, Emprendimiento y Educación · 2020
Typearticle
Languagees
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceCapital (architecture)Welfare economicsGeographyEconomicsArt
DOInot available

Abstract

fetched live from OpenAlex

espanolLa expresion economica de las migraciones, particularmente internacionales son las remesas. En general, estas se destinan para cubrir las necesidades basicas de consumo, sin embargo los participantes en el Programa de Trabajadores Agricolas Temporales (PTAT Mexico–Canada) las destinan entre otras cosas, a la educacion de los hijos y de la familia del migrante. El objetivo de este trabajo es conocer, a partir de un modelo logit y con base en la teoria del capital humano1 , si participar en este Programa ayuda a la formacion de capital humano de los migrantes mexiquenses en el PTAT y su familia. Los resultados indican que el PTAT es formador de capital humano en las familias vinculadas, ya que las remesas -en conjunto con el cumulo de conocimientos y experiencias- incrementan la propension a invertir en educacion formal y tambien los migrantes pueden replicar el conocimiento adquirido en Canada para sus labores agricolas en Mexico. Las variables que impactan en forma positiva a la formacion de capital son la permanencia y duracion del migrante en el programa (entre 7 y 12 anos ininterrumpidos) y la que agrupa el ingreso neto que va de los CAD$10,000 a CAD$14,000. Sin embargo, los que tienen mas de 43 anos de edad, los que previamente migraron a USA y los que provienen de la zona sur del estado de Mexico no son formadores de capital humano. La base de datos se obtuvo a traves de una encuesta aplicada en el 2011 a 67 migrantes participantes en el programa en el estado de Mexico: 64 hombres y tres mujeres. EnglishThe 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.015
GPT teacher head0.294
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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