Not without them: realising the sustainable development goals for women migrant workers
Bibliographic record
Abstract
Drawing on multiple data sources, including key informant interviews, participant observation and archival study, this paper provides an analysis of the civil society’s role in foregrounding the agenda of women migrants in migration and development (M&D) fora, and reflects on its role in realising the UN Sustainable Development Goals (SDGs). Yet, the dominant narrative within the state-led Global Forum on Migration and Development (GFMD) tends to be a gender-blind migration for development approach, which emphasises national-level economic growth at the centre of migration processes, while negating the subjectivities of women migrants and neglecting their contributions to the global economy; this approach diverts attention to a narrow focus on macro-economic development through forms of financial remittances. Based on an examination of the GFMD as a site for gender mainstreaming M&D, we reflect on lessons learned as we look forward to achieving the SDGs. We argue that while the SDGs include some significant provisions for women in migration, only critical civil society advocacy and activism networked within grassroots organisations can address the structural changes necessary (such as a re-articulation of the care economy to value economic contributions of women’s reproductive work) to transform and improve the lived realities of women in migration and realise the SDGs in a manner that fosters their empowerment.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".