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Record W2808005168 · doi:10.5430/jct.v7n1p197

Researching Gender Professions: Nurses as Professionals

2018· article· en· W2808005168 on OpenAlexvenueno aff
Benjamín Zufiaurre, Maider Pérez de Villarreal Zufiaurre

Bibliographic record

VenueJournal of Curriculum and Teaching · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsConstructiveFace (sociological concept)Health professionsWork (physics)Social workWelfareNursingHealth professionalsSocial WelfareState (computer science)PsychologyMedical educationSociologyPolitical scienceMedicineHealth careSocial scienceLaw

Abstract

fetched live from OpenAlex

Nurses as professionals of health, childhood education teachers, social workers and caregivers, join a group of“feminine professions” which grew through policies of a welfare state in postwar constructive period, or in times ofpostwar accords (Jones, 1983). These professions are under challenge because of neoliberal policies and practices inthe 21st century. In the paper, we want to give lights to the contradictory situations nurses face, as workers and ascare keepers. Nurses, suffer of a combination of public and private functions, at work, at home, and when caringfamily relatives. The way women feel about their role as professionals, and as women and workers, is illuminative,as we enquired in a funded research developed with nurses in the community of Navarra, Spain, first from 1993 to1996, and next, checking a continuity each ten years, 2006 and next 2016.

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)0.020
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0100.028
Scholarly communication0.0100.019
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.470
Teacher spread0.417 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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