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Record W2086266716 · doi:10.1353/jowh.2014.0027

Getting to the Heart of Science: Rosalie Bertell’s Eco-Feminist Approach to Science and Anti-Nuclear Activism

2014· article· en· W2086266716 on OpenAlexaboutno aff
Lisa Rumiel

Bibliographic record

VenueJournal of women's history · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsObjectivity (philosophy)SociologyIndigenousDisciplinePoliticsEnvironmental ethicsEpistemologySocial scienceGender studiesPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

This article focuses on Rosalie Bertell’s activist work with Indigenous communities in the Marshall Islands, Canada, and the United States. It examines how Bertell’s religious identity and her involvement in the eco-feminist, social justice, and anti-nuclear movements influenced her to develop a distinct approach to epidemiology. Bertell drew upon eco-feminist philosophy to challenge predominant ideas about scientific objectivity and detachment as they developed in modern epidemiology. She adopted a situated approach to epidemiology by relying on her expertise in biostatistics and incorporating a multi-disciplinary set of tools for perceiving radiation damage in the body to do small-scale community health studies. Bertell’s study model was shaped by the specific environmental health concerns of communities, designed to encourage community involvement, and intended for use as a political tool. Most significantly, with it she challenged the notion that scientists could achieve scientific objectivity only through detachment from the subjects of one’s analysis.

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)0.009
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.034
Scholarly communication0.0070.005
Open science0.0020.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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