Antiracism in appreciative inquiry: Generative tensions and collective reflexivity
Bibliographic record
Abstract
Appreciative inquiry is an action research methodology focused on revealing an organization's positive core. As a cross-racial team of antiracist researchers, we were drawn to appreciative inquiry due to its congruences with community-based research perspectives on power-sharing and co-constructing knowledge. Our collaborative reflexivity brought us to question whether Appreciative inquiry's hyper-focus on positivity would fit our antiracist research paradigm. We articulate reflections of how antiracism theory informed our approach to Appreciative inquiry in a study on the experiences of predominantly racialized settlement workers in schools during the COVID-19 pandemic. We explain how we negotiated tensions between Appreciative inquiry's focus on positivity and our antiracist framing, in a Canadian settler colonial context where institutional expectations to ignore racism and collapse diversity, loom large. Without a theoretical framework that attends to racism and power, Appreciative inquiry may not fulsomely address participants' transnational knowledges, nor experiences outside of a positive/negative binary. In our elucidation of how critical reflexivity on racism allowed us to integrate antiracism into Appreciative inquiry, we demonstrate the value of first-person action research for expanding the social justice aims of research.
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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 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.130 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".