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Record W2170580565 · doi:10.33524/cjar.v13i2.37

UNDERSTANDING PARTICIPATORY ACTION RESEARCH: A QUALITATIVE RESEARCH METHODOLOGY OPTION

2012· article· en· W2170580565 on OpenAlexaffvenue
Cathy MacDonald

Bibliographic record

VenueThe Canadian Journal of Action Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsQualitative researchParticipatory action researchAction researchFocus groupCitizen journalismAction (physics)FeelingData collectionParticipant observationEngineering ethicsSociologyPsychologyManagement sciencePedagogyPolitical scienceSocial psychologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Participatory Action Research (PAR) is a qualitative research methodology option that requires further understanding and consideration. PAR is considered democratic, equitable, liberating, and life-enhancing qualitative inquiry that remains distinct from other qualitative methodologies (Kach & Kralik, 2006). Using PAR, qualitative features of an individual’s feelings, views, and patterns are revealed without control or manipulation from the researcher. The participant is active in making informed decisions throughout all aspects of the research process for the primary purpose of imparting social change; a specific action (or actions) is the ultimate goal. The following paper will contextualize PAR in terms of its history, principles, definitions, and strengths, as well as discuss challenges and practical suggestions for using PAR. In addition, it will examine focus groups and interviews as methods for data collection, the role of PAR in education, and the types of research for which PAR is best suited.

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)0.223
metaresearch head score (Gemma)0.157
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.223
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2230.157
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0070.014
Scholarly communication0.0140.015
Open science0.0050.011
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0110.002

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 head0.999
GPT teacher head0.861
Teacher spread0.137 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations844
Published2012
Admission routes2
Has abstractyes

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