Bibliographic record
Abstract
The interview describes the integration of Appreciative Inquiry (AI) into the strategic planning cycle at Medicine Hat College. Appreciative Inquiry can play a powerful role in initiating and managing change through the process of asking generative questions. AI increases the possibility of introducing successful and transformative change at all levels within an organization. The interview was conducted in December 2015 by Innovations in Practice Editor Jennifer Easter. Dans l’entretien, il s’agit de l’intégration de l’enquête appréciative (Appreciative Inquiry) en cycle de planification stratégique au Medicine Hat College. L’enquête appréciative peut jouer un rôle vigoureux dans l’initiation et la gestion de changement par le processus de poser des questions génératrices. L’enquête appréciative augmente la possibilité d’introduire le changement réussi et significatif à tous les niveaux d’une organisation. L’entretien a été mené en décembre de 2015 par Jennifer Easter, la rédactrice d’Innovations in Practice.
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.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".