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Record W2025988070 · doi:10.1515/ijnes-2012-0015

Writing History: Case Study of the University of Victoria School of Nursing

2013· article· en· W2025988070 on OpenAlexaffabout
Margaret Scaia, Lynne Young

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCurriculumIdeologyLegitimacyFeminismSociologyPedagogyCurriculum developmentEngineering ethicsNursingPolitical scienceMedicineGender studiesLawPoliticsEngineering

Abstract

fetched live from OpenAlex

A historical examination of a nursing curriculum is a bridge between past and present from which insights to guide curriculum development can be gleaned. In this paper, we use the case study method to examine how the University of Victoria School of Nursing (UVic SON), which was heavily influenced by the ideology of second wave feminism, contributed to a change in the direction of nursing education from task-orientation to a content and process orientation. This case study, informed by a feminist lens, enabled us to critically examine the introduction of a "revolutionary" caring curriculum at the UVic SON. Our research demonstrates the fault lines and current debates within which a feminist informed curriculum continues to struggle for legitimacy and cohesion. More work is needed to illuminate the historical basis of these debates and to understand more fully the complex landscape that has constructed the social and historical position of women and nursing in Canadian society today.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0340.006
Scholarly communication0.0050.003
Open science0.0030.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.324
GPT teacher head0.554
Teacher spread0.230 · 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.

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

Citations2
Published2013
Admission routes2
Has abstractyes

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