Bibliographic record
Abstract
This thesis is a study of the economic, cultural, and political impact of video games in Canada. The trajectory of video games will be mapped beginning with the arrival of video games in Canadian markets in the mid-1970s and ending in the 2000s, with the debut of the Globe and Mail’s gaming section and the finalization of Ubisoft’s $263 million deal with the Ontario Government. Through this journey, it will be shown that over this roughly thirty-year period, video games have become interwoven into the everyday life of Canadians. \n \nToday in Canada, many regard video games as important cultural objects and a growing economic sector, yet this was not always the case. The industry has managed to thrive thanks to government influences and arrivals of anchor studios at key times. Yet in the media and the government, video games were seen as a nuisance and threat to children for much of the 1970s, 1980s, and 1990s, and faced many opponents that urged them be banned or censored. Despite these scares, video games were able to grow and be consumed in Canada without the implementation of government restrictions. This history of video games in Canada argues that these factors were caused by a generation gap and larger moral panic, which dissipated after it grew into such a large enough industry and market, that it became lucrative to Canada as a whole. This research shows the reactions that disruptive technologies can instill into a nation and the lack of government involvement speaks to the influence that video games and its industry hold in Canada
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".