Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.042 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it