Renewable inequity? Women's employment in clean energy in industrialized, emerging and developing economies
Bibliographic record
Abstract
Women are globally underrepresented in the energy industry. This paper reviews existing academic and practitioner literature on women's employment in renewable energy in industrialized nations, emerging economies and developing countries. It highlights similarities and differences in occupational patterns in women's employment in renewables in different parts of the world, and makes recommendations for optimizing women's participation. Findings reveal the need for broader socially‐progressive policies and shifts in societal attitudes about gender roles, in order for women to benefit optimally from employment in renewables. In some industrialized countries, restructuring paid employment in innovative ways while unlinking social protection from employment status has been suggested as a way to balance gender equity with economic security and environmental protection. However, without more transformative social changes in gender relations, such strategies may simply reinforce rather than subvert existing gender inequities both in paid employment and in unpaid domestic labor. Grounded interventions to promote gender equality in renewable energy employment – especially within the context of increasing access to energy services for underserved communities – are more prevalent and better‐established in some non‐OECD (Organisation for Economic Co‐operation and Development) countries. OECD countries might be well‐advised to try to implement certain programs and policies that are already in place in some emerging economies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".