Using Participatory Action Research to Connect Research Agendas with User Needs: A Crowdsourcing Case Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".