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Record W7132946416

Occupational segregation situations faced by Mexican migrant women hosted by the Seasonal Agricultural Workers Program in Canada during the years 2015 - 2017

2024· article· es· W7132946416 on OpenAlexaboutno aff
Zamir Ejaz Khan Torres, Natalia Andrea Navas Pinzón

Bibliographic record

VenueRepositorio Institucional Universidad El Bosque · 2024
Typearticle
Languagees
FieldSocial Sciences
TopicMigration, Health, Geopolitics, Historical Geography
Canadian institutionsnot available
Fundersnot available
KeywordsMigrant workersContext (archaeology)PopulationInequalityPoliticsIntersectionality
DOInot available

Abstract

fetched live from OpenAlex

El programa PTAT, es establecido entre los gobiernos de México y Canadá para generar oportunidades de empleo a través de un modelo de movilidad laboral legal. En este contexto, el estudio tiene como objetivo conocer las situaciones de segregación ocupacional que enfrentaron las mujeres migrantes mexicanas entre los años 2015-2017 acogidas por el programa. A través de la metodología cualitativa, con enfoque hermenéutico, y mediante un estudio de caso se explora la perspectiva de las mujeres migrantes mexicanas participantes en el programa PTAT, revelando así los significados subyacentes de sus vivencias en un contexto migratorio y laboral. Se reconocen conceptos como la relación laboral, derechos fundamentales, desigualdad y discriminación laboral y el enfoque feminista en las migraciones, en donde en los resultados se evidencian situaciones de maltrato, abuso y discriminación hacia la mujer en el programa. Se concluye que la segregación laboral persiste para mujeres migrantes mexicanas en el programa PTAT entre Canadá y México, pues a pesar de las medidas de protección y vinculación que se garantiza para ambos géneros, los estereotipos de género aún presentes pueden limitar las oportunidades laborales de las mujeres dentro del programa.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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Same venueRepositorio Institucional Universidad El BosqueSame topicMigration, Health, Geopolitics, Historical GeographyFrench-language works237,207