MIGRATION AND GENDER IDENTITY IN THE RURAL PHILIPPINES
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Bibliographic record
Abstract
Abstract Remittances associated with labor migration have been hailed by many as the most direct form of development to rural migrant-sending areas of the Global South, but focusing only on the quantity of cash flows does little to contextualize how migration has transformed social structures in rural areas. Through a qualitative focus on divisions of agrarian labor and decision-making, this article illustrates how the out-migration of men from rural areas of the Philippines is challenging preexisting gender ideologies of agricultural labor. The article examines how wives “left-behind” by their migrant husbands negotiate forms of farm work and responsibility that are culturally prescribed as “masculine.” While a number of studies have detailed how female migration can destabilize conventional gender roles—as housebound husbands are shown to take up social reproductive work often considered “feminine”—the impacts of male migration on the participation of housebound wives in productive farming practices has been less studied. This article presents several vignettes of stay-at-home mothers who venture into farming and it analyzes how these women interpret their own gender identity. ACKNOWLEDGMENTS I am grateful to Mariedeth Abustan, Brian Villaverde, and Thatchenko Roussini Reyes for their assistance in the field. I also wish to thank Doracie Zoleta-Nantes, Philip Kelly, Cindy Fan, Rebecca Elmhirst, and Patrick Oabel, who offered constructive comments on an earlier draft of this article. Support for this research has been provided by The Challenges of the Agrarian Transition in Southeast Asia (ChATSEA) program, a major collaborative research initiative funded by the Social Sciences and Humanities Research Council of Canada. Notes 1. Castles and Miller 2008. 2. Rigg Citation2005, 174. 3. Eder Citation2006, 397; see also Breman and Mundle 1991; Rigg Citation1998; Mulder 1997. 4. Go 2002; Kelly Citation2000; Le Espiritu 2003. 5. Tyner Citation2004; Parreñas Citation2000; Asis Citation1995. 6. Bagasao Citation2007. 7. Levitt Citation1998, Citation2001; Pedraza 1991; Pertierra 1992. 8. Butler 1990, 34. 9. Ibid., 33. 10. Chant and Radcliffe 1992; Pedraza 1991; Margold Citation1995. 11. Chant and McIlwaine 1995. 12. Gibson, Law, and McKay Citation2001. 13. Ibid., 380. 16. Ibid. 14. McKay Citation2003. 15. McKay Citation2005, 89. 17. Kelly Citation2000. 18. Pingol Citation2001. 19. Ibid., 33. 20. Ibid., 35. 21. Connell 2001. 22. Pingol Citation2001, 251. 23. Pingol Citation2001; see also Asis Citation1995. 24. The most basic, community-level political unit of the Philippine governmental system; the primary planning and implementation unit of broader government programs. For a more in-depth account of the barangay's history, operation, and organization see Ayson and Abletez (Citation1985). 25. National Statistics Office Citation2007. 26. Municipalities in the Philippines are divided into income classes according to the average annual income during the last three calendar years. 27. For comprehensive analyses of agrarian reform in the Philippines, see Borras (2008) and Putzel and Cunnington (1989). 28. A more detailed discussion of the difficulties associated with assigning household headship and membership follows in the subsequent discussion. 29. While rice and coconut represented the two most prominent crops being grown by Lucbanins —both in terms of the number of hectares devoted to these crops, as well as their income contribution to local residents—a range of fruits and vegetables were also cultivated. Most common among these were bananas, balinghoy or kamoteng kahoy (cassava), kamote (sweet potato), sayote (a local pear-shaped vegetable), kalabasa (squash), and various “Chinese” vegetables, as they are locally referred to. These leafy green vegetables are varieties of what are commonly referred to as “bok-choy” or “Chinese cabbage” throughout North America. 30. Goss and Lindquist Citation1995, 328; see also Hart 1992. 31. Eder Citation2006, 400.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it