Shaping policy in the Anthropocene: Gender justice as a social, economic and ecological challenge
Bibliographic record
Abstract
Environmental pressures such as natural disasters, resource scarcity, and conflict related to climate change have emphasized the importance of considering social justice within its ecological context. Gender inequality is one type of injustice that has traditionally been addressed as a social matter, yet gendered divisions in bargaining power, mobility, and access to resources are exacerbated by environmental instability. One barrier to gender equity in the face of a changing climate is the mainstream economic paradigm, which promotes growth and individualism, often at the cost of environmental and social wellbeing. The issue of gender inequality in the Anthropocene, the proposed geological epoch highlighting human impact of earth systems, is explored here in three parts. The first section identifies opportunities for feminist and ecological economics to assimilate notions of justice in mainstream economic thought. The second considers dynamics of gender equality through an econometric analysis of macroeconomic effects of traditionally female-dominated unpaid care work. Finally, the third part investigates national progress toward the maternal mortality reduction target set in the United Nations' Millennium Development Goals and proposes a gendered perspective for the newly implemented Sustainable Development Goals. The dissertation concludes with a discussion of policy implications for national and international development institutions as they seek to improve gender equity in diverse social and ecological contexts.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".