Bibliographic record
Abstract
My first encounter with the use of film in management teaching came in 2000 at the Academy of Management meeting in Toronto. Joseph (Joe) E. Champoux ran a professional development workshop on the use of film in management education. About 40 people were dotted around a large, spacious room listening intently to what Joe had to say. He showed clips from four or five films and explained how he used them to illustrate theory in his teaching. It was an interesting and engaging session, as Joe’s sessions always are. At an appropriate moment, probably when Joe was changing tapes over, I introduced myself to the chap sitting behind me. I asked him if he used films in his teaching. His response succinctly captured my own situation: “No. I’d love to, but I wouldn’t be taken seriously by my colleagues. I’m hoping no one saw me come in.” As I looked around the room, I sensed furtiveness in the rest of the audience as if the people were attending some form of illicit entertainment. Everyone seemed to sitting individually, quietly, desperately trying not to attract attention to themselves. This attitude toward the use of film in management education was replicated in the articles of the time. In these, judging by the published output,
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".