Gendered Paths to Civic Engagement and Leadership Among the Mexican Diaspora Living in the United States
Bibliographic record
Abstract
Gendered Paths to Civic Engagement and Leadership among the Mexican Diaspora in the United States. The integration of immigrant populations into the civic society of their countries of destination is a topic of increasing global concern as the forces of globalization, natural disaster and conflict encourage ever greater numbers of people to migrate. This paper addresses this topic by studying the paths to leadership of those in the Mexican diaspora in the United States who have been elected to be members of an advisory council to the Mexican government, the Consejo Consultivo del Instituto de los Mexicanos en el Exterior or CC-IME. In 2003, the Mexican government established an advisory counsel of approximately 120 prominent members of the Mexican American community living in the United States. Mexican consulates in the United States and Canada were instructed to hold elections to choose representatives of their diasporic communities for a three year term. Since 2003, CC-IME has had three cohorts of around 120 advisors elected in 2003, 2006 and 2009 respectively. Advisors must be fluent in Spanish. Drawing on this sample of diasporic leaders, this study uses in-depth interviews and focus groups to study the routes to civic participation and leadership with particular attention to gendered differences. We expect our findings to shed light on successful strategies and to offer examples for others to emulate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".