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Record W2171568123 · doi:10.1177/0193945906287706

Doing Participatory Action Research in a Racist World

2006· review· en· W2171568123 on OpenAlexaff
Colleen Varcoe

Bibliographic record

VenueWestern Journal of Nursing Research · 2006
Typereview
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsParticipatory action researchCitizen journalismDominance (genetics)InterrogationRacismSociologyAction (physics)Power (physics)Public relationsFace (sociological concept)Action researchSocial psychologyPsychologyPolitical scienceGender studiesSocial sciencePedagogy

Abstract

fetched live from OpenAlex

This exploration of the racial power dynamics in a participatory action research project with women who had experienced intimate partner violence discusses the challenges inherent in doing participatory action with antiracist intent and offers suggestions for overcoming these challenges. To engage in this type of research, explicit commitment to the goals of an antiracist intent needs to be shared as widely as possible. Fostering such shared commitment demands that the social locations of all involved be interrogated continuously. Such interrogation, however, needs to be prefaced with understanding that individuals are not representative of particular power positions or social identities or locations and with critical attention to how language and social structures shape racism and other forms of dominance. Being inclusive must be understood as complex and the influence of diverse agendas and perspectives acknowledged and taken into account. In the face of such complexity, "success" in research may need redefinition.

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)0.045
metaresearch head score (Gemma)0.041
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.045
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0040.009
Scholarly communication0.0070.006
Open science0.0030.005
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.963
GPT teacher head0.813
Teacher spread0.149 · 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
GenreReview

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

Citations47
Published2006
Admission routes1
Has abstractyes

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