LEARNING FROM ECOFEMINISM: DECONSTRUCTING THE DUALISTIC ‘SOFT’ VS. ‘HARD’ NATURE OF ENGINEERING EDUCATION
Bibliographic record
Abstract
There has been little progress in increasing the diversity of engineering over the past three and a half decades. Much of the diversity work in engineering has an implicit liberal feminism and fails to deconstruct the hierarchical social categories and the underlying ideals of engineering culture. There is a growing need to critically look at the embedded culture of engineering and how this presents a barrier to diversity. This paper provides a critical review of key ecofeminist literature and how engineering education can learn from ecofeminist approaches. The ecofeminist framework aims to breakdown dualisms that artificially separate humans and nature, and rather emphasizes the essential interdependence of all organisms. The aim of this work is to better understand how ecofeminism could be used as a framework to change the culture of engineering education to create a more inclusive environment and foster a greater holistic skillset in our students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".