Community Engagement in a Time of Confinement
Bibliographic record
Abstract
This article examines the significant constraints on, the necessity for, and the opportunities around community engagement in a time of confinement. We consider the compounded challenges faced by marginalized communities in the context of the coronavirus disease 2019 pandemic, and we follow this with reflections on the triumphs and tensions of emergent engagement practices. We then describe four exercises that we conducted before the onset of the pandemic in a research project exploring public engagement from the ground up in relation to policy-making, and we suggest how the lessons learned may be applied to contemporary decision making. Our overall goal is to illustrate how and why community engagement is particularly pressing in the current crisis, as pandemic restrictions have added new dimensions to long-standing practices of containment. We argue that although these most recent forms of engagement are contested and complex, they are essential to ensuring that policy-making is built on processes of equity, access, and inclusion.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.056 | 0.045 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".