Bibliographic record
Abstract
The digital economy is expanding access to global markets and changing the way Canadians access content, order taxis, find accommodations and shop for goods. It has also made it possible to purchase digital goods and services over the Internet directly from suppliers located outside Canada just as easily as from domestic vendors. While this is useful for consumers, it complicates tax collection and raises competitive pressures for both domestic and foreign businesses. In particular, providers of digital products and services, ranging from e-books and online games to streaming services such as Netflix and Spotify, are not obligated to collect and remit sales tax if they are not “carrying on business” in Canada. Instead, the consumers of the service are responsible for determining and paying the associated GST/HST, though in practice they rarely do. This creates two major problems: Canadian businesses are being put at a disadvantage relative to their foreign competitors who are not paying GST/HST and governments are missing out on significant amounts of tax revenue. To address both problems, Ottawa should amend the Excise Tax Act to apply to businesses that supply digital goods and services for consumption within Canada regardless of where the company is located, in compliance with International VAT/GST Guidelines. There are many countries already employing policies that balance both coverage of the digital economy and the reporting requirements they impose on foreign businesses. Canada can learn from these policies and implement changes that work with our existing excise tax regulations. Delaying policy changes only prolongs the disadvantages that Canadian businesses face within their own borders and leaves tax revenue on the table at the expense of the Canadian economy.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".