MétaCan
Menu
Back to cohort
Record W2090963422 · doi:10.1177/1476750306066804

Finding the ‘action’ in feminist participatory action research

2006· article· en· W2090963422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAction Research · 2006
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of British ColumbiaBritish Columbia Centre of Excellence for Women's Health
FundersSimon Fraser UniversityMichael Smith Health Research BC
KeywordsFieldnotesParticipatory action researchAction researchCitizen journalismAction (physics)SociologyPublic relationsSet (abstract data type)Political sciencePedagogyEthnography

Abstract

fetched live from OpenAlex

Although feminist researchers have increasingly called for participatory and action-oriented research, there have been few analyses of the diverse actions that can occur. We theorized the actions considered and implemented in a feminist participatory action research project (FPAR). For three years we collaborated intensively with a group of diverse women on low income who were involved in a FPAR project designed to reduce social isolation and other self-identified health problems. Our data set included tape recordings of 32 one-on-one interviews, 15 research meetings, and extensive fieldnotes. Our findings indicated that actions occurred on both individual and collective levels; some had been enacted prior to the project and were shared to promote ongoing or new actions, while others arose as a consequence of the women’s involvement in the project. Additionally, some actions were implemented and actualized while others, though discussed at length, remained hopes for the future. While the research participants reported the benefits of being involved in such projects, they also spoke of the potential risks. Our findings revealed the complexities of taking action in FPAR and highlight important considerations for others wishing to engage in this type of research.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.002

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 head0.892
GPT teacher head0.730
Teacher spread0.162 · 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