Achieving the sustainable development goals: surfacing the role for a gender analytic of migration
Bibliographic record
Abstract
This paper forms the introduction to the Special Issue: Achieving the Sustainable Development Goals (SDGs) through the Gender, Migration and Development Nexus. This article takes a broad look at the changing dynamics of migration and development through the feminisation of globalised labour flows and the gendered experiences of categorisation by states and multilateral bodies, and the gender-specific vulnerabilities and outcomes of human mobility. We illustrate how a more nuanced approach to the SDGs that incorporates gender and migration is needed in order that policy and programming designed to achieve the 2030 Agenda is accurately informed and appropriately framed. In this paper and this Issue, we argue, that it is necessary to confront the SDGs with a deeper understanding of gender, migration and development in order to illuminate the interconnected globalised and transnational realities of gendered labour flows. With this aim in mind, we look to civil society participation and the role of the existing human rights architecture, as the key to ensuring that a deep, wholistic and ultimately universal application of the SDGs can be achieved addressing those populations whose rights to development have been undermined by dint of their migration or flight and applying a gender analysis to our understanding of migration and development.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".