Bibliographic record
Abstract
Few Canadian digital policy issues have proven as confusing as the ongoing debate over digital taxation. While there is general agreement that a neutral tax policy should apply to the online world, the issue has been muddled by both nomenclature and corporate efforts to use digital tax policy for competitive advantage. With politicians fearing voter backlash over the perception of increased taxes, Canadian digital tax policy has struggled to keep pace, leading to a predominantly hands-off approach. The result is an uneven digital policy playing field that leaves domestic firms disadvantaged and government coffers missing out on hundreds of millions of dollars. This chapter seeks to unpack the digital tax policy debate by examining the various meanings, the core policy choices, and the potential to develop a fair digital policy structure. The chapter begins with a discussion of digital sales taxes, followed by corporate income taxes, and the finally mandated contributions by companies active in the digital economy, including online service providers and Internet access providers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".