Learning to “Walk the Talk”: Reflexive Evaluation in Community-First Engaged Research
Bibliographic record
Abstract
While a considerable body of literature advocates for participatory evaluation methodologies within community-centred community-campus engagement (CCE) projects, there has been limited study to date on how a “community-first”, or community-driven approach to CCE may be informed and strengthened by reflexive evaluation practices. Reflexive evaluation involves a critical reflection on the positionality of participants in relation to the processes they are engaged in and attempting to influence. In response to this gap, this article develops a reflexive account of our activities and influence, as academics, within an evaluation of the first phase of the multi-year pan- Canadian CCE project known as Community First: Impacts of Community Engagement (CFICE). Building on the experiences of community and academic partners across a collective reflective evaluation of over forty demonstration projects within Phase I of CFICE, we reflexively examine our own efforts to incorporate common community-first CCE working practices into the evaluation processes to which we contributed. This examination reinforces scholarly assertions about the crucial position of community voices in co-governance of CCE projects, the need to reduce institutional constraints to community participation, and the value of nourishing relationships within CCE work. The approach explored in this article complements more general evaluation methods for practitioners seeking to ensure accountability to community-first values in their work. The article also explores how reflexive evaluation can inform practitioners about deeper personal and collective introspection and transformations related to relationships and processes associated with employing community-first CCE working practices.
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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 | 0.540 | 0.568 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.017 | 0.087 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".