Formulation of a framework to support female workers and a comparative policy study from the perspective of carbon neutrality-just transition-gender equality nexus
Bibliographic record
Abstract
In long-term strategies seeking carbon neutrality (CN) in response to climate change, societies need to make the transition to a low-carbon economy. However, during this transitional process, some people, regions, and sectors can be adversely influenced and left behind. Thus, the concept of “just transition” (JT), which emphasizes a fair and inclusive approach for those who are marginalized by such transitions, has arisen. Marginalized groups are often segmented into women, indigenous people, racial groups, or immigrants for the purposes of targeted support. Specifically, gender equality has been considered in climate policies, particularly focusing on women. However, there is a lack of research and policies that consider women working directly or indirectly in the phase-out industry toward CN. Therefore, this paper attempts to explore whether and how the European Union (EU), the United States (US), and Canada consider working women in their JT policies for CN. As an analytical framework, this paper sets four policy elements required to achieve balance at the intersection of CN, JT, and gender equality. These four policy elements include: i) consideration of JT and gender (women) in national policies, ii) representation of women in decision-making processes, iii) consideration of gender (particularly working women) in redistribution policies in phase-out industry, and iv) consideration of gender (women) in opportunity access to new jobs in new industries. In analysis with these policy elements, it was found that gender is being considered in national policies, decision-making processes, and opportunity access to new jobs in new industries as part of JT policy, despite some differences among the EU, the US, and Canada. However, gender is not commonly considered in the redistribution policies in the phase-out industry. This result is compared with the Korean case. This paper concludes with policy implications for Korea’s gender consideration in JT policies for CN.
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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 | 0.041 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".