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Record W4411501301 · doi:10.1177/16094069251354847

Reimagining Community and Knowledge: Emerging Directions in Participatory Research

2025· article· en· W4411501301 on OpenAlexaff
Ronald Yesudhas, Bala Raju Nikku

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsAccountabilityParticipatory action researchSociologyCitizen journalismPublic relationsContext (archaeology)GrassrootsCommunity engagementPolitical scienceSocial accountingPositivismPolitics

Abstract

fetched live from OpenAlex

There is a global resurgence in the field of participatory research and community engagement. The world health system crisis post pandemic, geo-political inequalities, social exclusion and ecological degradation expose the limits of development and the dominant positivist paradigm of research which captures the social world as ‘fact’ intact. Traditional top-down research known for upward accountability to donors and institutions of power, seldom take stance and support downward and lateral accountability (towards participants/ community and the researchers). There is a growing recognition of the need for approaches which focuses on participants lived experiences, share power and build research with rather than on communities. In the emerging context, learning institutions, social justice philanthropists, and grassroots community organisations are increasing calling for models of inquiry that are not only methodologically rigorous but also socially just and locally rooted. This special edition on “Participatory Research and Community Engagement” captures this pivotal moment in the evolution of research practice. The papers presented here reflect a cross-section of the dynamic efforts to respond to the complex glocal social issues through inclusive, contextually grounded, and collaborative approaches. Together, they represent a shift- as Thomas Kuhn puts it from extractive model of knowledge production and ‘development-by-accumulation’ view of science to those that prioritises co-creation, accountability and relational ethics.

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)0.059
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.961
GPT teacher head0.817
Teacher spread0.144 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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