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Record W2544447834 · doi:10.1057/978-1-137-40523-4_2

Critical Participatory Action Research

2016· book-chapter· en· W2544447834 on OpenAlexaff
Robin McTaggart, Rhonda Nixon, Stephen Kemmis

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParticipatory action researchCitizen journalismAction (physics)Communicative actionAction researchSolidarityPublic relationsSociologyCollective actionSocial practicePolitical scienceSocial sciencePedagogyLawPolitics

Abstract

fetched live from OpenAlex

Critical participatory action research emerges from critique of conventional social and action research, recognizing that action research itself is a social practice—a practice changing practice. It arises when people share concerns and work together to make their individual and collective practices less irrational, unsustainable, and unjust. By participating in public spheres , participants create communicative action and communicative space —clarifying their concerns, informing changes in their practices, and creating communicative power and solidarity . Participants’ own analyses of their practices are supported by understanding practice architectures , local arrangements enabling or constraining their work. Changing a practice also involves changing practice architectures. Critical participatory action research differs from other research traditions because it supports participants changing “what is happening here ” in disciplined, prudent, and informed ways. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.065
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0080.024
Scholarly communication0.0150.011
Open science0.0040.013
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0300.006

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 head0.684
GPT teacher head0.619
Teacher spread0.066 · 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
DomainMethods
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

Citations63
Published2016
Admission routes1
Has abstractyes

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