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Record W2037590267 · doi:10.1111/nin.12076

Social inclusion/exclusion as matters of social (in)justice: a call for nursing action

2014· article· en· W2037590267 on OpenAlexafffundabout
Sharon Yanicki, Kaysi Eastlick Kushner, Linda Reutter

Bibliographic record

VenueNursing Inquiry · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
FundersCanadian Nurses FoundationUniversity of Lethbridge
KeywordsConceptualizationSociologySocial exclusionSocial philosophyInclusion (mineral)CitizenshipDistributive justiceEconomic JusticeSocial psychologyPublic relationsPolitical scienceSocial scienceSocial relationPsychologyLawPolitics

Abstract

fetched live from OpenAlex

Social inclusion/exclusion involves just/unjust social relations and social structures enabling or constraining opportunities for participation and health. In this paper, social inclusion/exclusion is explored as a dialectic. Three discourses--discourses on recognition, capabilities, and equality and citizenship--are identified within Canadian literature. Each discourse highlights a different view of the injustices leading to social exclusion and the conditions supporting inclusion and social justice. An Integrated Framework for Social Justice that incorporates the three discourses is developed and used to critique the dominant focus on distributive justice within foundational Canadian nursing documents. We propose a broader conceptualization of social (in)justice that includes both relational and structural dimensions. Opportunities for multilevel interventions to promote social justice are identified. This framework is congruent with nursing's moral imperative to promote health equity and with the multiple roles played by nurses to promote social justice in everyday 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 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)0.042
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0430.191
Scholarly communication0.0300.025
Open science0.0060.029
Research integrity0.0140.023
Insufficient payload (model declined to judge)0.0050.000

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 head0.254
GPT teacher head0.544
Teacher spread0.290 · 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 designNot applicable
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

Citations49
Published2014
Admission routes3
Has abstractyes

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