Bibliographic record
Abstract
Fitzgerald, Oliver & Hoaxey’s paper, in my opinion, is the finest critique yet written on appreciate inquiry. They help us to think deeply and avoid simplistic notions of “positive” and “negative”, reminding us that holding too tightly to decontextualized assertions of what is positive can get in the way of the very things AI aspires to do. I comment on how AI’s original intent of studying the “life giving properties” of social systems got translated into studying “the positive”. The paper also offers a new contingency for understanding why AI succeeds or fails: the extent to which dreams, aspirations and expression of positive affect are censored by the organization. I question whether all transformational change processes are inherently counter cultural and if so, would AI be useful in a positively deviant organization. Finally, the paper reminds us that superb scholarship on organizational change is most likely to come from those fully engaged in the practice of it.
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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.069 | 0.075 |
| Insufficient payload (model declined to judge) | 0.005 | 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".