Transnational migration and Mexican women who remain behind: An intersectional approach
Bibliographic record
Abstract
OBJECTIVE: To explore the scope, range, and nature of the existing literature on Mexican women who remain behind in their communities of origin while their partners migrate abroad. DESIGN: A scoping review informed by an intersectionality framework was conducted over four months, January-April 2020. DATA SOURCES: The electronic databases Medline, PsyINFO, Global Health, CINAHL, Gender Studies Database, Dissertations & Theses Global, LILACS, IBECS, and Sociological Abstracts were searched. REVIEW METHODS: Articles were included if they focused on Mexican women who remain behind across transnational spaces. Two independent reviewers screened and selected articles. Data were analyzed and synthesized using descriptive statistics for quantitative data and content analysis for qualitative data. RESULTS: A total of 19 articles were included for analysis; within those, the methods used included quantitative (n = 5), qualitative (n = 11), mixed methods (n = 2), and intervention (n = 1). Most studies lacked a theoretical framework (n = 10); the majority were empirical published studies (n = 11), and most used interviews (n = 12) and surveys (n = 6) to collect data. All of the articles studied cis-heterosexual Mexican women. Major areas identified were 1) research context, 2) gender roles, and 3) women's health. CONCLUSION: Implications for practice and future research are discussed.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".