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‘I await your apology’: a polyphonic narrative interpretation

2007· article· en· W2058880041 on OpenAlexaff
Penelope Cash

Bibliographic record

VenueNursing Philosophy · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFoucault, Power, and Ethics
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPerformative utteranceSociologyNarrativeConversationMeaning (existential)FeelingRealmSense of agencyEpistemologyPsychologySocial psychologyAestheticsLinguisticsLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

A patient's experience unfolds through a nurse's personal conversation with herself. Conveyed through three voices, the nurse's dialogue highlights her many internal struggles; those with her conscience on what she understands to be best practice, those important to her as a person, those of an ethical nature that profoundly affect one's search for meaning, and those in the personal-professional realm driven in part by institutional culture. These multivoiced knowledges are confronted in ways that foreground language and understanding as performative acts. At the same time, another journey is co-constructed with the reader, one that weaves in-between the symbolic and the real, engaging the imaginary in (inter)play. The nurse's response to the inner conversation with her 'self/selves' problematizes practice, illuminates the patient's perspective while highlighting the nurse's sense of her marginal position. Insight into reified and hegemonic assumptions, strategies of how control is maintained through organizational surveillance, trust and moral agency help to foreground personal expectations as the nurse begins to grapple with her own feelings of betrayal. Tackling these insights offers opportunities to rethink oppressive practices in the provision of care. It also enables an alternative appreciation of the everyday dilemmas confronting nurses and offers new meaning to practice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.406
Teacher spread0.352 · 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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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