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Radical women in social work: A historical prespective from North America

2018· article· en· W2903726083 on OpenAlexaff
Therese Jennissen, Colleen Lundy

Bibliographic record

VenueAotearoa New Zealand Social Work · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsSocial workSociologyStatus quoRacismPovertySocial issuesPolitical scienceUnemploymentSocial changeGender studiesSocial justiceEconomic growthCriminologyLawEconomics

Abstract

fetched live from OpenAlex

INTRODUCTION: Many challenges that confront social workers today are similar to problems they have faced over the past century – inequality, poverty, unemployment, militarisation and armed conflict, and the challenges of refugee resettlement, to name a few. It is instructive for contemporary social workers to revisit this history and to determine if there are lessons to inform our current struggles.METHOD: This paper explores the issues faced and strategies employed by radical, politically active social workers, most of them women. These social workers had visions of social justice and were not afraid to challenge the status quo, often at very high personal costs. The radical social workers were expressly interested in social change that centred on social justice, women’s rights, anti-racism, international peace, and they worked in close alliance and solidarity with other progressive groups.CONCLUSIONS: This article highlights the work of five radical female social workers. Radical social workers were in the minority but they were extraordinarily active and made important contributions in the face of formidable challenges.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0270.019
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0030.005
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 head0.029
GPT teacher head0.318
Teacher spread0.289 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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