An analysis of the potential influences of misinformation on Alberta’s public health response to the Covid-19 pandemic and what this tells us about policy creation in the age of social media
Bibliographic record
Abstract
The COVID-19 pandemic exposed a variety of previously unrecognized problems and showed how new aspects of our growing culture and world have complicated all aspects of our lives. One issue that was both an old problem we learned more about and a new evolution that we were rudely awakened to is misinformation. This paper through the use of a literature review, analysis of government actions, and covid era misinformation, sought to examine if misinformation affected the pandemic response from the Canadian province of Alberta. After reviewing what misinformation was present with several trends in public health response as well as data related to the efficacy of the Albertan public health response and criticism of its failures, this paper believes that there is a high likelihood that there was an influence from misinformation. This influence started early on, causing hesitation to put in place health orders, and an extreme eagerness to lift them. It then slowly grew and mutated, potentially resulting in the ousting of one premier and their replacement with a leader who is significantly more beholden to misinformation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".