The journey of Central American women migrants: en<i>gender</i>ing the mobile commons
Bibliographic record
Abstract
This article delves into the concept of the ‘mobile commons’ which is articulated within the Autonomy of Migration (AoM) approach. The AoM literature focuses on migrant agency by advocating that migrants practice ‘escape’ and ‘invisibility’. However, drawing on the stories of women migrants from the Northern Triangle of Central American (NTCA) (El Salvador, Guatemala, Honduras) travelling through Mexico, this article aims to engender and thereby trouble the concept of the mobile commons by questioning several taken-for-granted assumptions that are based on gender-neutral knowledge and dichotomous ways of thinking. Using women’s experiences to question the assumptions made with respect to ‘migrant knowledge’, I show that the knowledge among women migrants from the NTCA is influenced by gendered power imbalances that place women in subordinate positions. The analysis will first focus on explaining the mobile commons as a theoretical concept. Following this, I discuss how conceptualizing the mobile commons through a feminist perspective challenges the ideas of invisible knowledge and trust often integral to the ways in which the concept of the mobile commons is used. Finally, I outline the survival strategies that migrant women may use given their own knowledge of the migration context in Mexico, and reflect on what this means for the scholarly understanding of the ‘mobile commons’.
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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 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".