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Record W2373936131 · doi:10.1177/1077800415625689

Toward a Narrative Ethics

2016· article· en· W2373936131 on OpenAlexaff
Shaun Stevenson

Bibliographic record

VenueQualitative Inquiry · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsIndigenousNarrativeBioethicsAutonomySociologyKinshipEnvironmental ethicsIntersubjectivityAnthropologySocial sciencePolitical scienceLawEcology

Abstract

fetched live from OpenAlex

Drawing on experience of community-based health research with a First Nation population, I present a case for the incorporation of an ethics grounded in narrative, particularly within Indigenous community-based research (CBR). Viewing conventional bioethics’ emphasis on individual autonomy as increasingly insufficient in grappling with the complexities of research with Indigenous communities, with their often historically, socially, and culturally specific notions of kinship, intersubjectivity, and relationality, I suggest that an ethics of narrative has the potential to respond to a conventional bioethics of autonomy in ways that would be more commensurable with the ethical and lived experiences of Indigenous persons. Drawing on poststructuralist and Indigenous thought, I ultimately argue for the narrative competency to engage with and respond to the stories of sickness and health that may arise from cultural contexts other than those sedimented within Western Euro-American frameworks. Key to this endeavor is attention to and understanding of conceptions of self and community formation as multiple and diverse, marked by porosity and even the potential for ethical failure.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.339
GPT teacher head0.520
Teacher spread0.181 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2016
Admission routes1
Has abstractyes

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