A Feminist Ethics of Care Within Counterspaces: Supporting Inclusion in Postsecondary ICT Education
Bibliographic record
Abstract
There is a dichotomy in computing education. The information communication technology (ICT) field, which includes all areas that pertain to technology, computing, or computational reasoning (e.g., computer science, computer engineering, and machine learning), needs diversity to thrive. Yet, the undergraduate programs that support the field have a difficult time attracting and retaining that diversity[1]. In undergraduate education, ICT is one of the most exclusionary of the science, technology, engineering, and mathematics (STEM) cultures[2]for women. Exclusion challenges a woman’s sense of belonging[3]and identity—her sense of “personal relevance, ownership, and integration into the sense of self”[4, p. 208]. Significantly, women’s intersectional identities (e.g., the intersection of race, gender, and disability) frame their experiences with oppression, causing problematic experiences in ICT education to compound and hurt computing identity[5].
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.072 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".