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Record W3089696002 · doi:10.1093/sw/swaa034

Advancing Reproductive Justice to Close the Health Gap: A Call to Action for Social Work

2020· article· en· W3089696002 on OpenAlexaff
Anu Manchikanti Gómez, Margaret Mary Downey, Emma Carpenter, Usra Leedham, Stephanie Begun, Jaih Craddock, Gretchen E. Ely

Bibliographic record

VenueSocial Work · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOppressionPraxisSociologyScholarshipDignityReproductive healthReproductive rightsSocial workEnvironmental ethicsEconomic JusticeReproductive justiceCriminologyGender studiesPolitical scienceLawPoliticsPopulationBiology

Abstract

fetched live from OpenAlex

Reproductive justice is an intersectional social movement, theory, and praxis well aligned with social work's mission and values. Yet, advancing reproductive justice-the right to have children, to not have children, to parent with safety and dignity, and to sexual and bodily autonomy-has not been a signature area of scholarship and practice for the field. This article argues that it is critical for social work to advance reproductive justice to truly achieve the grand challenge of closing the health gap. The article starts by discussing the history and tenets of reproductive justice and how it overlaps with social work ethics. The authors then highlight some of the ways by which social workers have been disruptors of and complicit in the oppression of individuals, families, and communities with regard to their reproductive rights and outcomes. The article concludes with a call to action and recommendations for social work to foreground reproductive justice in research, practice, and education efforts by centering marginalized voices while reimagining the field's pursuit of health equity.

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)0.115
metaresearch head score (Gemma)0.079
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.115
Threshold uncertainty score0.608

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.079
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0430.168
Scholarly communication0.0390.046
Open science0.0070.048
Research integrity0.0480.069
Insufficient payload (model declined to judge)0.0120.002

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 head0.097
GPT teacher head0.411
Teacher spread0.314 · 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
GenreCommentary

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

Citations29
Published2020
Admission routes1
Has abstractyes

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