Bibliographic record
Abstract
Abstract Despite deliberate moves to integrate gender with climate change solutions, efforts do not go far enough to account for coloniality, thus falling short of achieving feminist, just and transformative ends. Coloniality is a political blind spot and a systematic amnesia in climate policies and actions, despite being a key driver of climate change manifested through various forms of extractivism, economic growth, and hegemonic Eurocentric knowledge production. As a corrective and a pathway toward realizing a post/decolonial feminist climate praxis, I will disclose the colonial underpinnings in (i) a persistent gender binary and women‐centered approaches; (ii) white feminist epistemic privileging; and (iii) acquiescing to masculine Enlightenment‐inspired techno‐managerialism. Furthermore, disclosures of colonial erasures entail a foundational re‐evaluation of the climate change narrative from an isolated form of natural crisis to a phenomenon embedded in complex histories of colonialism, extractivism, and capitalist exploitation that threatens the intrinsic interdependence of nature and society and planetary survival. As a result, a post/decolonial feminist climate praxis then asks that we foster and restore this interdependence by institutionalizing an ethics of socioecological care, acknowledging epistemic diversity through embodied knowledge, and carrying out intersectional justice. This article is categorized under: Climate, Nature, and Ethics > Climate Change and Global Justice Climate and Development > Sustainability and Human Well‐Being Climate and Development > Social Justice and the Politics of Development
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".