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Record W4390117017 · doi:10.33524/cjar.v23i3.645

Using Participatory Action Research to Connect Research Agendas with User Needs: A Crowdsourcing Case Study

2023· article· en· W4390117017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Action Research · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsCrowdsourcingCitizen journalismKnowledge managementStakeholderParticipatory action researchAction researchProcess (computing)Public relationsAction (physics)Stakeholder engagementBusinessSociologyPolitical scienceComputer scienceWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

Crowdsourcing is a digital method used in business and academia to engage public participation in the provision of services, ideas, or information. This original case study focuses on examining process-based challenges of combining knowledge and skills of diverse crowdsourcing stakeholders in a network for shared learning. Participatory action research (PAR) was selected as the method to reflect all stakeholder agendas during the network’s formation. Findings demonstrate a shift in emphasis from initially complying with university funding criteria, to meeting the group’s desire for a network that encourages collaboration and capacity development for its users. This result advances understanding of deploying PAR to foster collaboration between crowdsourcing stakeholders for the purpose of forming a sustainable network for shared learning, and thereby informs future critical research pertaining to crowdsourcing policies and 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.

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)0.051
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.021
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0000.004
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 head0.806
GPT teacher head0.579
Teacher spread0.227 · 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