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Record W2766399021 · doi:10.1177/0160449x17733106

Obstacles to Nurses’ Labor Militancy in Central America: Toward a Framework for Cross-National Comparison of Nurses’ Collective Action

2017· article· en· W2766399021 on OpenAlexaff
Lisa Kowalchuk

Bibliographic record

VenueLabor Studies Journal · 2017
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Guelph
FundersPan American Health Organization
KeywordsAngerSocializationPoliticsFace (sociological concept)Political actionCollective actionAction (physics)Political scienceFocus groupSociologyGender studiesPsychologySocial psychologySocial scienceLaw

Abstract

fetched live from OpenAlex

This paper seeks to understand the low level of nurses’ labor militancy in El Salvador and Nicaragua compared with many other countries. Key to the analysis is the concept of oppositional consciousness, which was developed for the study of how oppressed groups convert anger over unjust treatment into vocal and even disruptive demands for change. I use data collected through interviews and focus groups to argue that while nurses in El Salvador and Nicaragua face many of the same hindrances to militancy seen elsewhere, they are more exposed to cultural and institutional forces that discourage a contestational stance. Chief among these are the influence of religion in nurses’ schooling and socialization, and nurses’ lack of experience with unions specific to their occupation. The latter owes, in turn, to particular historical and political factors in each country.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0060.009
Scholarly communication0.0050.004
Open science0.0010.008
Research integrity0.0010.001
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 head0.120
GPT teacher head0.483
Teacher spread0.363 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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