Feminist Ethics in Universities: How to Make Timely Decisions that Represent Community Values
Bibliographic record
Abstract
Abstract: In practical terms, decisions on various academic matters in universities are complex and reflective of the views held by those in positions of power. For a more egalitarian view derived from a feminist perspective, this paper proposes an alternative form of decision-making. An underlying moral code and its corresponding decisions influence policies and the broad spectrum of educational futures in universities. According to Walker (2007), theoretical-juridical models, which have dominated ethical understandings, assume a prevailing moral code that embodies universal ethical principles and applies to all people in every jurisdiction at all times. By contrast, embodied perspectives that recognize the relationality of networked participation enliven Walker’s expressive-collaborative model (ECM) through moral conversations and negotiations among moral agents who are members of that specific community. The authors cite the #femedtech networked participatory community as an embodiment of feminist values that the ECM proposes and describe #femedtech’s value activity and code of conduct generation activity. This paper applies feminist ethics to ameliorate universities’ policy-making process. The paper builds the case for university community values to guide university decisions and advocates strategic trust as an essential criterion to uphold communal values. Finally, the paper concludes with practical propositions that reflect a commitment to democratic, deliberated, and inspiring process-based decision-making responsive to a diverse community’s moral lives and ethical needs.
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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 | 0.069 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.072 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".