The Roles and Responsibilities of Action Research Networks in Times of Crisis: Lessons from the Action Research Network of the Americas
Bibliographic record
Abstract
Against the backdrop of the COVID-19 pandemic, we explore the perceived roles of action research networks during times of crisis and then consider our own experiences grappling with our responsibilities as members of the Action Research Network of the Americas (ARNA) in highlighting and building solidarity through its Knowledge Democracy Initiative and Social Solidarity Project. To critically reflect on our work, we consider the usefulness of Gaventa’s (1991) three strategies for knowledge democratization to action research networks in “perilous” times and the responsibility of action research scholars-advocates-activists-participants to anchor our work in an ethos of knowledge democracy. In conclusion, we issue a call to embrace critical, participatory forms of action research, and creative, new pathways for the work of knowledge democracy.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.044 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".