Bibliographic record
Abstract
In acknowledging the help and support from friends, acquaintances, and colleagues throughout the long process of writing and editing a book, one always risks omitting some.I hope that those of you who are omitted will forgive me for any oversight.First of all, I would like to thank Andrzej Wajda himself, who knew about my project from its inception in 1998 and who fully supported my efforts, providing access to his archives, explaining to me details of his life, and offering many useful comments regarding the production aspects of his films.Second, I would like to thank Scott Holden for his patient examination of the text for cohesiveness, accuracy, and stylistic consistency.Third, I would like to thank the Social Sciences and Humanities Research Council of Canada and the Dean of Arts at the University of Western Ontario for supporting this work with generous grants.During the preliminary planning stages of this project, I encountered a great deal of enthusiasm on part of both English-and Polishspeaking film academics.Leading film academics in Poland or those of Polish descent in Canada and the United States were instrumental in my completion of this book.In particular, Prof. Alicja Helman from the
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.262 | 0.178 |
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".