Misinformation & Disinformation in Canadian Society. \nA system analysis & futures study
Bibliographic record
Abstract
Misinformation and disinformation online is one of the great problems of our time. The digital era has enabled new and increasingly complex communication systems to flourish. Information flows across vast distances instantly and people are more interconnected than ever before. This also means that information which is inaccurate, misleading or objectively false can also travel at unprecedented rates, and often travels faster and farther than objectively truthful content. This information contaminates the online landscape and impacts people’s ability to discern accurate and truthful content. Misinformation and disinformation is often more sensationalized, which often leads to it being engaged with more often on platforms, this can in turn cause it to become favoured by algorithms. These algorithms tend to prioritize popular content to maintain users on platforms longer and exposes them to more advertisements, in order to gain advertising revenue. \nThis research used interviews, a survey and an extensive literature review to understand the spread of misinformation and disinformation in Canadian society today, and to map this using a systems approach. Following this, strategic foresight tools were used to generate potential future scenarios with the goal of making strategic recommendations for the current context.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".