Subverting Democracy to Save Democracy: Canada’s Extra-Constitutional\tApproaches to Battling “Fake News”
Bibliographic record
Abstract
Nearly a decade after the first Twitter and Facebook revolutions, the early narratives pointing to social media as a great agent of democratization1 have given way to a more nuanced understanding of the impact of the Internet on our political discourse. While there is no question that Internet access provides tremendous expressive benefits, scholars are increasingly questioning whether this information diet is ultimately healthy for society. An analogy to sugars, fats and salts has emerged, where just as an appetite for rich foods served our species well when resources were scarce, but have become a liability in an age of plenty, our natural curiosity and hunger for new ideas has led to problems in an online world teeming with misinformation and extremism. The potential for mischief was illustrated in stark terms in 2016, when well documented campaigns of foreign interference, including social media manipulation, impacted both the Brexit referendum and the United States presidential race. In response to these, and other high profile cases of the Internet being used as a vector to distribute manipulative content, many countries around the world are re-examining their approach to regulating online speech, as well as their specific posture on electrion speech. However, while there is no question that the Internet has created a raft of new regulatory challenges, it is important not to lose sight of the foundational importance of freedom of expression to an effective democracy. Solutions which attempt to address the challenges posed by online speech, but which do this by undermining core freedom of expression principles, are ultimately destined to be counter-productive. Any victory against forces that threaten our democracy will be hollow if it comes at the cost of the human rights principles which undergird our democratic system.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 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".