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Record W4212970343 · doi:10.31234/osf.io/2934c

Human as relation: An exploration of the ethics of care

2022· preprint· en· W4212970343 on OpenAlexaff
Kira Brunner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsAlienationMoralityRelation (database)Ethics of careConsequentialismNatural (archaeology)EpistemologyNormative ethicsEthical theoryEthical theoriesSociologyPsychologySocial psychologyEnvironmental ethicsPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

This essay describes the ethics of care, a philosophical theory originally born out of psychological studies on development. For much of psychological history, women were seen to be ‘less developed’ on standardized tests of morality. In response, Carol Gilligan produced a study that examined how women’s conceptions of moral responsibility differ from men, especially in terms of care, connection, and interdependence. This relationality has been developed into a complex ethical theory which this paper opposes against consequentialism and Kantian ethics; two ethical theories that instead favour impersonality. Further, it is argued that not only is unequal interdependence central to human experience; it is also a natural human drive to care for ourselves and the people closest to us. Ethical theories that oppose this fail to serve their purpose in providing a framework that is possible for humans to follow and must make concessions or sub- justifications to avoid complete alienation in their followers. With two case examples, this essay establishes the ethics of care within the psychological development framework and argues for its superiority as compared to impersonal ethical theories.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.049
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.502
GPT teacher head0.624
Teacher spread0.122 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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