Bibliographic record
Abstract
I would like to express my gratitude to Murray Pomerance, editor of the exceptional Horizons of Cinema series at SUNY Press, for his long-term friendship and deeply encouraging support of my writing on film.He convinced me by his excitement during our initial conversation on the topic of "theatrical space in film" that there was a book in it, and that I must go ahead with it.Over the next two years, he continued to push me, with his special brand of insistence, to stay the course.It is difficult for me to believe that I have been directing and acting in plays for more than a half century.In the course of this inexhaustibly exhilarating education, I have had ample opportunity to think practically and theoretically about the nature of theatre space and its diverse uses.Gilberto Perez, in his study of the rhetoric of film, The Eloquent Screen, provides some phrases about rhetorical "figures of arrangement" onscreen that apply equally well to our engagement with space onstage: "where it puts us, how it orients us, how it makes us feel" (xxi).He also reminds us of the importance of "spectator specifics" in any consideration of the deployment of film technique.What film form is able to do to spectators, what it enables them to experience in a narrative, is crucially dependent on what they "bring to it" (xxi).I have had a great many enlightening conversations over the years with theatre collaborators, valued colleagues at the University of Manitoba, and students who have shaped my thinking about the theatrical issues I investigate in this book.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.331 | 0.212 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".