Pausing in the pandemic: Using a co-inquiry approach to advance relational and reflective learning in a university-community partnership
Bibliographic record
Abstract
This article demonstrates a reflective and collective inquiry process among eight stakeholders of a multi-year partnership between a university social innovation center and a youth play non-profit during year one of the COVID-19 pandemic. We asked whether “pausing” a University-Community project for reflection and recalibration was an ethical response to disaster contexts. Drawing from cooperative inquiry (CI) and collaborative developmental action inquiry (CDAI) methodologies, as well as co-authorship and reflective journaling, we developed a co-inquiry process that revealed the disparities between University and Community actors within a long-term partnership. Co-inquiry helped us reattune to power-sharing goals of participatory action research as we explored new modes of engagement through progressive rounds of loop-learning. While the pandemic exacerbated unilateral patterns of engagement that plague partnerships, it created an opportunity to prioritize relationship-rebuilding and frame-creation. We found that co-authorship was methodologically important for facilitating co-inquiry and that pausing and holding space for this shared reflection was a key driver of learning.
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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 | 0.066 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.005 | 0.008 |
| 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".