Filipino Migrant and Returnee Nurses Resisting and Adapting to the Pressures of Becoming “Ideal Migrants”
Bibliographic record
Abstract
Abstract This paper scrutinizes the desirability and feasibility of return migration for Filipino male and female nurses, while considering “turning points”, factors such as natural disasters, here supertyphoon “Yolanda” or “Haiyan”, and/or family crises and changes in the family structure, which affect the migration trajectory. The Philippines has a long history of outward migration, more recently of female workers employed in the healthcare and domestic sectors: this longstanding phenomenon being encouraged by a “sophisticated infrastructure” which expects surplus workers to become “ideal migrants” — that is, compliant and aspirational. We take an intersectionality approach, considering gender, race, ethnicity, and other social divisions which place migrant and returnee nurses in (dis)advantageous positions, in order to explore the nurses’ own strategies of resistance and adaptation towards becoming “ideal” workers and sustaining the “ideal migrant” trajectory of upward social mobility. While the participants of this study had varied reactions towards the feasibility of permanent return, this paper offers policy recommendations on supporting the reintegration of returnee migrant nurses, providing a more nuanced understanding of circular and return nurse migration, and that of nurses’ negotiations and agency towards navigating their own and others’ expectations of being “ideal” migrants.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".