Bibliographic record
Abstract
An increasing number of Canadians are concerned about how the COVID-19 crisis was handled by our governments and institutions. We are alarmed by the serious consequences of their decisions and, at times, their apparent indifference to the costs. Those consequences include tragic impacts on the personal lives of many, violations of constitutionally guaranteed rights and freedoms in the name of health security, and economic impacts of lockdown measures, which subjected millions of Canadians to business closures, loss of income, and unemployment.Canadians are asking many questions: Were the measures taken by governments in Canada appropriate to the perceived threat? Were they based on sufficient clinical and statistical evidence? Were they suitably focused? How effective were they? Were there any conflicts of interest at play? Was there enough emphasis on prevention and early treatment? On informed consent? Was sufficient debate permitted? In attempting to prevent COVID-19, what other maladies were we ignoring or fostering? Did the public health interventions, such as mandatory vaccinations, cause more harm than good?These concerns have given rise to a growing demand for an Independent National Inquiry into the management of the COVID-19 crisis in Canada. To encourage and inform such an inquiry, from June 22nd – 24th 2022, the Canadian Covid Care Alliance, in partnership with the Canadian Adverse Event Reporting System (CAERS), Fearless Canada, United Healthcare Workers of Ontario and the Frontier Centre For Public Policy among others, sponsored a cross-country live streamed event moderated by a diverse panel of experts to:Hear testimony illustrating the harms that have resulted from government policies implemented to cope with the COVID-19 outbreak;Receive scientific, medical, and legal testimony as to alternative approaches that were ignored - or even condemned - which might have been pursued;Generate recommendations to ensure that Canadians never again experience the degree of loss, trauma and disruption caused by the official response to COVID-19.A Citizens’ Hearing consists of testimonies challenging the official responses of Canada’s federal and regional governments and recommendations for better handling the next public health crisis, should one of such a scale occur again.Canada’s response to COVID-19 has been far from perfect. We can and should learn from our mistakes. The landscape of this enormous challenge has been and is constantly changing. A Citizens’ Hearing aims to contribute to a national conversation of truth and understanding that might lead us to a new resilience and emergency preparedness. To face the next health crisis, we must change the narrative from one of fear and reaction to one of confidence in a properly managed, proactive and nuanced emergency management process that reacts to real world data, and keeps dialogue and consultation with a cross-section of stakeholders open and transparent.
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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.070 | 0.018 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 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".