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Record W3130564138 · doi:10.31219/osf.io/rf7xa

The Impact of COVID-19 on Public Space: A Review of the Emerging Questions

2020· review· en· W3130564138 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2020
Typereview
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsUniversity of ReginaVancouver Community CollegeUniversity of British Columbia
Fundersnot available
KeywordsPublic spacePandemicSocial distanceCoronavirus disease 2019 (COVID-19)Space (punctuation)Public healthDistancingPopulationPublic relationsPerception2019-20 coronavirus outbreakOrder (exchange)Political scienceEconomic growthBusinessPsychologyEconomicsComputer scienceMedicineEnvironmental healthEngineeringArchitectural engineeringOutbreak

Abstract

fetched live from OpenAlex

Restrictions on the use of public space and social distancing have been key policy measures to reduce the transmission of SAR-CoV-2 and protect public health. At the time of writing, one half of the world’s population has been asked to stay home and avoid many public places. What will be the long term impacts of the COVID-19 pandemic on public space once the restrictions have been lifted? The depth and extent of transformation is unclear, especially as it relates to the future design, use and perceptions of public space. This article aims to highlight emerging questions at the interface of COVID-19 and city design. It is possible that the COVID-19 crisis may fundamentally change our relationship with public space. In the ensuing months and years, it will be critical to study and measure these changes in order to inform urban planning and design in a post-COVID-19 world.

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.

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)0.011
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0460.043

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 head0.073
GPT teacher head0.418
Teacher spread0.345 · 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