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Record W4293795640 · doi:10.35844/001c.36761

Assembling a Cabinet of Curiousities: Using Participatory Action Research and Constructivist Grounded Theory to Generate Stronger Theorization of Public Sector Innovation Labs

2022· article· en· W4293795640 on OpenAlexaff
Lindsay Cole

Bibliographic record

VenueJournal of Participatory Research Methods · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGrounded theoryBricolageTransformative learningSociologyMetaphorParticipatory action researchAction researchFraming (construction)Citizen journalismEpistemologyQualitative researchEngineering ethicsKnowledge managementPedagogySocial scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper describes how a critical qualitative bricolage of research methods, with participatory action research and constructivist grounded theory at the center, were assembled and applied to support stronger theorization of the work of public sector innovation labs while remaining strongly grounded in the experiences and intelligence of practitioners. We begin by sharing the context for this research, including describing what PSI labs are, the purpose for this research, and an overview of the process and participants. Next, the framing for this approach is described, detailing the metaphor of assembling a cabinet of curiosities. This cabinet contains five main methods and approaches including: critical research bricolage; sensitizing concepts; participatory action research; constructivist grounded theory; and weaving the assemblage together. We conclude by discussing the four key methodological insights generated , the contribution that this work makes to the literature about participatory research methods, and how researchers with a transformative intent can use this in practice.

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 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)0.067
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0670.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.801
GPT teacher head0.582
Teacher spread0.219 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations7
Published2022
Admission routes1
Has abstractyes

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