Achieving the sustainable development goals: surfacing the role for a gender analytic of migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 | 0.000 |
| 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