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Record W7053538497

The âWill to Participateâ: Governmentality, Power, and Community-Based Participatory Research

2016· article· en· W7053538497 on OpenAlexaff

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsParticipatory action researchGovernmentalityCitizen journalismPower (physics)Participatory GISReading (process)
DOInot available

Abstract

fetched live from OpenAlex

Although critiques of participatory development attend to knowledge/power, Anglo-American literature on community-based participatory research (CBPR) is largely silent on the politics of these collaborations.As the "will to participate" is increasingly normalized for communities and CBPR is naturalized as socially just research, it is crucial that we inquire into the uneven terrain of the collaborative encounter.The CBPR literature makes claims to emancipatory, empowering, and egalitarian relations that articulate a largely unproblematic, harmonious encounter.Yet these claims remain under-scrutinized.Furthermore, little is known of how participatory practices operate as a technique to access and appropriate community knowledge, while leaving power asymmetries intact.This paper deploys a Foucauldian governmentality framework to explore how CBPR's relations of power collude with the macro rationalities of the neo-liberalism, inclusive liberalism, and the moral imperialisms of the knowledge economy to produce participatory subjects and spaces "outside" of socio-political histories and presents.Critical reflections on two CBPR projects are woven into a theoretical reading that explores the limits of participatory approaches and its uneven power relations as ethical problems.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.286
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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