1979 : reading the tax-shelter boom in Canadian film history
Bibliographic record
Abstract
More certified-Canadian feature films were shot in Canada in 1979 than in any other year. The height of what has become known as the "tax-shelter boom," 1979 stands as a remarkable moment in the history of the Canadian cinema, with 70 features shot in a year in which Hollywood produced only 99 films. The extant history of the Canadian cinema has largely ignored this moment, and in this thesis I argue that the slim treatment of the period by critics represents a "received wisdom," consistently repeated, but seldom scrutinized, and that this received wisdom is representative of the culturally nationalist impulse which has coloured the entire historiography of the Canadian cinema. Because many of the films produced during the boom were in the style of Hollywood genres, the "received wisdom" presents the entirety of the tax-shelter boom as a cultural and industrial near-disaster for the Canadian cinema, and this thesis, partly a revisionist history, explores not only those conclusions, but also provides critical discussion of them. I begin by presenting the received wisdom, the existing account, on the period. This is followed by a chapter which situates the tax-shelter boom in a history of state intervention in the feature film industry. Following this, I provide analysis of the contexts surrounding the tax-shelter boom, including critical discussion of articles and reviews from the contemporaneous popular press, and of the industry discourse. I then turn my attention to the texts themselves, which the received wisdom more or less ignores, and provide three thematically-organized chapters of textual analysis: the first organized around readings of gender and genre in the films, the second on the prevalent theme of "selling out," which is central to numerous films of the period, and a third chapter which explores the place of Quebec in the films of the period. The thesis concludes with an analysis of the material effects of the government policies which led to the boom, and concludes that in this respect too, the received account of the period---once again, as a failure---needs to be reexamined.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".