Reimagining intersectionality in environmental and sustainability education: A critical literature review
Bibliographic record
Abstract
We seek to understand how issues of intersectionality are addressed in environmental and sustainability education (ESE) literature, focusing on how gender is discussed in relation to other social identities such as class, race, sexuality, and ability. Our analysis draws from feminist and decolonizing frameworks, and uses intersectionality to examine how ESE literature addresses issues as interconnected. Intersectional analysis originates from Black feminist perspectives on how social identities/subjectivities collide and collude to reproduce systemic and unique forms of oppression. This article contributes to this critical framework by incorporating considerations of Indigenous interconnectivity and land-based sovereignties. We begin this literature review by providing a background of intersectionality and interconnectivity from Black feminist and Indigenous knowledge systems, and describe how these frameworks inform our analysis. We then review existing ESE literature to critically examine how researchers have utilized feminist perspectives to discuss gender in relation to class, race, sexuality, body size, and ability as well as species. This review seeks to disrupt marginalization and calls for the use of critical frameworks such as intersectionality to deconstruct and disrupt oppression in ESE.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".