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Record W3119450116 · doi:10.3224/ijar.v16i3.06

Repoliticising Participatory/Action Research: From Action Research to Activism: some considerations on the 7th Action Research Network of Americas Conference

2021· article· en· W3119450116 on OpenAlexaboutno aff
de Castro Pitano Sandro, Rosa Elena Noal, Cheron Zanini Moretti

Bibliographic record

VenueIJAR – International Journal of Action Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchCitizen journalismAction (physics)Action researchPolitical scienceSociologyRelation (database)Media studiesPublic relationsLawAnthropologyPedagogy

Abstract

fetched live from OpenAlex

The seventh conference of the Action Research Network of the Americas (ARNA) took place in Montreal, Canada, from the 26th to 28th of June, in 2019. Having as title “Repoliticising Participatory/Action Research: From Action Research to Activism”, the event gathered people from different areas of practice coming mostly from the North American countries: Canada, United States and Mexico. The discussion presented here is based on notes made by the authors in the course of the conference, in which 40 words/keywords were identified, serving as a base to debate the validity of the principles of participatory research and action research in its repoliticisation and activism. Thus, we presented a systematisation of some key themes of the conference, among them, the commitment with the rupture: in relation to the traditional practices of research, the role and the social responsibility of the universities and the transforming character of participation, with emphasis in the effort for its repoliticisation and activism.

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)0.142
metaresearch head score (Gemma)0.046
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 score0.978
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1420.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0220.046
Scholarly communication0.0470.021
Open science0.0050.018
Research integrity0.0170.029
Insufficient payload (model declined to judge)0.0070.001

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 head0.927
GPT teacher head0.693
Teacher spread0.234 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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