Towards a community of care: Counterspaces for women in sTem education
Bibliographic record
Abstract
Despite significant research and intervention efforts there remain significant disparities with the retention of women and women of colour in postsecondary STEM programs. These programs are often inhospitable to women. STEM counterspaces are emerging as important communal spaces that support women and in their identity development in STEM. Counterspaces are community-driven, safe spaces that nurture a sense of belonging through strategies that are validating for intersectional identity development. In this paper, we review existing research about counterspaces that support women and women of colour in STEM. and use the framework of Feminine Ethics of Care to analyze these spaces. Much of the literature on counterspaces covers STEM in general, we therefore make connections to how counterspaces can be applied to ICT education in particular. We conclude the paper with recommendations for ICT education.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".