A multi‐scalar critical analysis of return migration policies in Mexico
Bibliographic record
Abstract
Abstract Services and resources for migrants returning to Mexico are necessary to ease their transition and “re‐integration” into home communities. Policies that do not have a holistic approach can result in serious implications for the social, political, cultural, and health of returnees, receiving families, and communities. This research critically analyses return migration policies in Mexico drawing from the intersectionality‐based policy analysis framework and a multi‐scalar approach to critically study return migration policies in Mexico. We analysed 20 return migration policies using the principles of the intersectionality‐based policy analysis framework. In 2021, we interviewed those impacted by return migration policies in Veracruz, Mexico to gain deeper insights into return migration policies. Women who stayed behind, return migrants, community leaders, and health‐care providers were interviewed via phone or face‐to‐face in Spanish. Information was transcribed verbatim and analysed with the aid of computer‐assisted data analysis software and quotes were translated into English. They shed light on two major inequities in policies: (1) the lack of acknowledgement of diversity or return migrants and (2) the exclusion of receiving families and communities from the “re‐integration” process of return migrants. Based on the multi‐scalar critical policy analysis, return migration policies in Mexico would benefit from a more comprehensive and inclusive approach where the needs of return migrants and community members are protected based on their diversity.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".