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Record W3017568585 · doi:10.1097/ans.0000000000000311

Hypervisible Nurses

2020· article· en· W3017568585 on OpenAlexaff
Amélie Perron, Trudy Rudge, Marilou Gagnon

Bibliographic record

VenueAdvances in Nursing Science · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of VictoriaUniversity of Ottawa
Fundersnot available
KeywordsIgnoranceWrongdoingEpistemologySociologyPsychologyPublic relationsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Whistleblowing has been examined from various angles over the past 40 years, but not yet as a matter of epistemology. Whistleblowing can be understood as resulting from the improper transmission of critical knowledge in an organization (eg, knowledge about poor care or wrongdoing). Using the sociology of ignorance, we wish to rethink whistleblowing and the failures it brings to light. This article examines how nurses get caught in the strategic circulation of knowledge and ignorance, which can culminate in acts of whistleblowing. The sociology of ignorance helps understand how whistleblowing is borne out of the complex and strategic circulation of knowledge and ignorance that spells multiple and intersecting epistemic positions for nurses. In particular, various organizational blind spots position nurses as untrustworthy and illegitimate speakers in the "business" of the organization. Organizational failings therefore remain concealed while nurses become hypervisible, both as faulty care providers and as problematic information brokers.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.022
Scholarly communication0.0110.010
Open science0.0010.015
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0120.003

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 head0.077
GPT teacher head0.561
Teacher spread0.484 · 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 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

Citations20
Published2020
Admission routes1
Has abstractyes

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