Bibliographic record
Abstract
This article empirically analyzes the process of setting copyright tariffs in Canada. It explains the national importance and international relevance of Canada’s tariff setting system, putting administrative issues in the context of broader legal, economic and copyright policy matters. It reviews relevant literature, synthesizing what research exists about this process. It schematically maps key statutory and administrative milestones. Based on a robust, empirical research method, empirical data was collected in respect of a fifteen-year period between and including 1999–2013, during which major legal reforms were implemented and the economic significance of copyright tariffs increased. The article reports statistical figures and trends related to copyright tariffs within the study period, especially regarding the time involved in Canada’s tariff-setting process. Eight-hundred-fifty-two different tariffs were certified by the Copyright Board during the study period. There are 209 pending tariffs that were proposed for that period that have not yet been certified. The certified tariffs took an average of 3.5 years to certify after filing. The average pending tariff has been outstanding for 5.3 years since filing (as of March 31, 2015). On average, tariffs are certified 2.2 years after the beginning of the year in which they become applicable, which is in effect a period of retroactivity. The standard deviation in the time from proposal filing to tariff certification is two years. A hearing was held in 28% of tariff proceedings. The average time from proposal filing to a hearing in those proceedings was just over three years. The average time from a hearing to tariff certification was almost 1.3 years. These empirical research findings deliver a unique understanding of Canada’s tariff-setting procedures, enabling policymakers as well as the Board to better respond to the needs and concerns of copyright stakeholders.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.038 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".