Bibliographic record
Abstract
Alberta’s magnificent landscape has served as a popular location for filmmakers since the dawn of the movie industry. For film pioneers, Alberta embodied the myth of the Great Northwest, a primeval mountain wilderness and the last western frontier. In turn, Canadian entrepreneurs were eager for American studios to drape Alberta landscape across the backdrop of their movies, an advertisement without equal. A Stunning Backdrop is the untold story of six rollicking decades of filmmaking in Alberta. Mary Graham draws on twelve years of exhaustive research to reveal a film history like no other, illuminating the deep importance of the province to Hollywood. She explores the often friendly partnerships between American filmmakers and Indigenous communities, particularly the Stoney Nakoda, that provided economic opportunities and, in many cases, allowed them to retain religious and cultural practices banned by the Canadian government. Beautifully illustrated with archival photography and featuring century-old set stills alongside photographs of the locations as they appear today, by Jean Becq, Solomon Chiniquay, Jeff Wallace, George Webber, and Paul Zizka, A Stunning Backdrop is the fascinating, often surprising, always unconventional story of film in a province whose rugged, compelling, multifarious, terribly beautiful landscape continues to inspire filmmakers and audiences around the world.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".