"Falsehood Flies, and the Truth Comes Limping After": Combatting Online Disinformation in the Shadow of CUMSA
Bibliographic record
Abstract
We live in an era of online disinformation. In the blink of an eye, any person can share a lie online with hundreds of people; within hours, that lie may have been seen by thousands or millions. Though the threat of online “echo chambers” has been exaggerated, the danger of online disinformation to informed voting has not. Each Canadian voter has a Charter right to be reasonably informed about candidates running for election, but online disinformation is threatening that right. The government of Canada may have a positive duty to protect this right; at the very least, it is a matter of good governance to counteract online disinformation. This obligation, however, is complicated by Canada’s ratification of CUSMA. Under article 19.17(2) of CUSMA, Canada has agreed to not pass laws that hold thirty-party platforms liable for content, including disinformation, posted on their websites. Article 19.17(2), however, must be interpreted narrowly in order to protection section 3 rights: though Canada cannot pass laws that hold thirty-platforms liable for user-generated content, it may create laws that hold such platforms liable for failure to remove user-generated disinformation. There are several tools the Canadian government may utilize to enforce such laws and combat disinformation generally.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.054 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".