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Record W4281738289 · doi:10.1111/cag.12769

The place of the public under COVID‐19

2022· article· en· W4281738289 on OpenAlexaffvenueabout
Vanessa Mathews

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPublic spaceSocial distanceCoronavirus disease 2019 (COVID-19)Context (archaeology)DistancingFace (sociological concept)Identity (music)Space (punctuation)Political sciencePublic healthSociologyPublic relationsGeographyPolitical economySocial scienceAestheticsMedicineEngineeringComputer science

Abstract

fetched live from OpenAlex

Over the past several decades, scholars have lamented the erosion of "true" public space through the rise of semi-public and private spaces where access is determined, and usage is increasingly regulated (e.g., through curfews and restrictions on use). Writing in the midst of the sixth wave of COVID-19, the importance of inclusive, open spaces in Canadian urban centres is evident: these are spaces that allow for movement and participation (ideally) across axes of identity and difference. Yet, public spaces are also bound up with the heightened regulation of bodies to supress contagion through social distancing and restrictions on use. While some bodies are in place (and take up space) in public in this context, other bodies face limitations pertaining to discrimination, surveillance, health, and access. This viewpoint highlights the importance of retaining and producing inclusive public spaces and the need to think critically about the urban experience under COVID-19.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.284
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0390.083
Scholarly communication0.0200.007
Open science0.0020.018
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.296
Teacher spread0.266 · 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 designQualitative
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

Citations1
Published2022
Admission routes3
Has abstractyes

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