Ecofeminisms and education: repositioning gender and environment in education
Bibliographic record
Abstract
This issue of Gender and Education explores aspects of the relationship of ecofeminisms and the environment to gender and education in the broadest sense.It provides an opportunity to re-think how ecofeminisms have, or could, inform educational theory and practice.In our call for papers we suggested the following questions as one way of sparking ideas for contributors:. How is ecofeminist thought currently being taken up in practice in diverse educational sites (e.g.early childhood, elementary, secondary and higher education, informal, community and adult education, activist learning, social learning, public pedagogies)? .How does ecofeminist-inspired education, training, or activist pedagogies perpetuate and/or disrupt dominant ideologies about gender and the marginalization of diverse voices? .What affinities and tensions are at play between ecofeminisms and feminist new materialisms, intersectionality, and/or posthumanism, and what might these imply for gender and education? .How could critical environmentally-oriented education movements and subfields (e.g.climate justice education, common worlds pedagogies, critical animal-focused education, critical food education, environmental justice education, Land education, queer ecopedagogies, etc.) be more informed by ecofeminism, and what directions could that take those fields? .What could ecofeminisms contribute to queer feminisms and what could queer feminisms contribute to ecofeminisms in the context of educational practice and theory?
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".