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Record W4323355195 · doi:10.32920/22227643.v1

Governments’ accountability for Canada’s pandemic response

2023· preprint· en· W4323355195 on OpenAlexafffundabout
Lara Khoury, Alana Klein, Marie-Eve Couture-Ménard, Kathleen Hammond

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversité de SherbrookeToronto Metropolitan UniversityMcGill University
FundersMcGill University Health CentreMcGill University
KeywordsAccountabilityPublic administrationLegislaturePolitical scienceLegislationDiscretionPandemicCorporate governancePoliticsState of emergencyState (computer science)LawBusinessCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic—with its wide-reaching social, political, and economic implications—showcases the importance of public health governance. Governmental accountability is at the forefront of societal preoccupations, as state actors attempt to manage the pandemic by using sweeping emergency powers which grant them significant discretion. Though emergency measures have tremendous impacts on citizens’ lives, elected officials and civil society have little input in how governments wield these powers. We reviewed available mechanisms in Canadian private, constitutional, and criminal law and found them to be unlikely sources of much-needed accountability. Therefore, we propose that provincial and territorial legislatures modify public health legislation to expand mechanisms to foster public confidence in decision-makers, and bolster accountability to parliaments and citizens.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.246
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0220.007
Scholarly communication0.0130.003
Open science0.0020.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.220
GPT teacher head0.522
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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