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Record W4401013613 · doi:10.55016/ojs/jet.v54i3.74687

Feminist Ethics in Universities: How to Make Timely Decisions that Represent Community Values

2022· article· en· W4401013613 on OpenAlexaff
Rahul Kumar, Giulia Forsythe

Bibliographic record

VenueJournal of educational thought. · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsBrock University
Fundersnot available
KeywordsSociologyNegotiationValue (mathematics)DemocracyPower (physics)Public relationsEngineering ethicsEpistemologyPolitical scienceLawPoliticsComputer scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.323
GPT teacher head0.423
Teacher spread0.100 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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