Bibliographic record
Abstract
Bill C-18, Canada's Online News Act, has sparked significant debate. It mandates tech giants like Google and Meta to compensate Canadian news outlets for using their content, aiming to support the declining news industry. Proponents argue it will correct revenue imbalances caused by digital platforms, ensuring fair compensation for media. However, critics, including tech companies, claim it's based on flawed premises, asserting that the bill won't resolve the industry's issues and could lead to censorship and reduced news access. This essay argues that Bill C-18 is ineffective, failing to address the fundamental challenges of the Canadian news industry's outdated business model. It also contends that the bill disproportionately benefits large media corporations and could hinder innovation, further entrenching the dominance of a few major players in the industry. Ultimately, the essay suggests that a more effective approach would involve adapting to digital media's realities, moving away from traditional revenue models to ensure the industry's sustainability.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".