A comparative case study of appreciative inquiries in one organization: implications for practice*
Bibliographic record
Abstract
Eight different sites in a large, Canadian urban school district engaged in an appreciative inquiry into “what do we know about learning”. Data collected over the following year indicate that four of the sites experienced transformational changes, two sites had incremental changes and two showed little or no change. This paper describes the AI intervention in detail and then explores differences in each site that may explain differences in level of change. The level of positive affect and ratings of success of the AI Summits at each site showed no meaningful relationship to change outcomes. Level of change did appear to be related to how generative the inquiries were, how well the Discovery phase was managed and the quality of Design statements that came out of the summits. Other factors exogenous to the design of the AI also appeared to play a role. These included relations between teachers and principals, credibility of local change agents, passionate and engaged leadership, and linkage to pre-existing, shared concerns. Recommendations for AI practice are given.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".