UNDERSTANDING CREATIVE CANADA: CULTURAL POLICY, PUBLIC SENTIMENT, AND DIGITAL TAX REFORMS
Bibliographic record
Abstract
In recent years, Canada legislated the most significant amendments to cultural policy in over a generation aimed at addressing a policy drift amid digital disruption. With wide criticism for these reforms, they are assumed have garnered negative reception in absence a digital tax; however, the legal intricacies of often inconsistent, and overlapping digital tax measures advocated for in Canada remain largely unexamined. \n \nAgainst this background, the OECD/G20 are anticipated to implement the most fundamental overhauling of the international tax system in over a century, with a focus on addressing the tax challenges arising from the digitalisation of the economy. Recognizing that for a solution to be delivered in the coming year, there will need to be a consensus reached by OECD/G20 member countries by July 2021, this study considers the contingency of effective reforms, and alternative measures under consideration by the Government of Canada. \n \nEvidence suggests that a solution to today’s digital tax challenges is perhaps a caveat for addressing the issues of Canada’s cultural policy that center upon its failure to keep pace with the digital creative economy. Observations consider the bearing equitable taxation has on the Canadian government’s general tax revenues necessary to fund direct spending programs. In order to link industry-specific government spending with industry-specific behaviour, underlying ties between new Canadian media and digital taxation are investigated, so as to examine opportunities for sustainable cultural policy and funding in the Canadian context.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".