Pausing in the pandemic: Using a co-inquiry approach to advance relational and reflective learning in a university-community partnership
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it