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Record W4401809788 · doi:10.55016/ojs/sppp.v13i1.70911

Mandatory Mask ByLaws: Consideration Beyond Exemption for Persons with Disabilities

2020· article· en· W4401809788 on OpenAlexaffabout
Jessica Kohek, Ash Seth, Meaghan Edwards, Jennifer Zwicker

Bibliographic record

VenueThe School of Public Policy Publications · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Mandatory mask bylaws are set to be instated in both Calgary and Edmonton, and other regions of Canada in efforts to reduce transmission of COVID-19. While mandatory mask bylaws are rooted in the interest of public safety, accessibility challenges for persons with disabilities need to be considered to ensure full participation in society. This communiqué highlights some key considerations for implementation of inclusive mandatory mask guidelines. The City of Calgary and other jurisdictions are implementing a bylaw that mandates face coverings be worn in public transit, public vehicles for hire, public indoor space and City public facilities. There are a number of important considerations for persons with disability in regard to acquisition of face masks, public participation, and enforcement of mask-wearing policies. Without inclusive design and clear communication, mandatory mask bylaws may produce numerous barriers to social re-entry for persons with disabilities. Such barriers include unequal access to face masks, social stigma, exclusion from public spaces, and disproportionate questioning or penalization (Williamson and Whaley 2020). As the economy re-opens, it is important to ensure the safety of all Albertans. While mandatory face mask bylaws have evidence to support effectiveness in slowing transmission, safe economic re-launch must be inclusive of persons with disabilities, a demographic that has been inequitably burdened by the effects of COVID-19 and unduly overlooked in pandemic response and recovery planning.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.265
Teacher spread0.172 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2020
Admission routes2
Has abstractyes

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