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Record W4226205069 · doi:10.1093/cjres/rsac017

Pandemic polycentricity? Mobility and migration patterns across New York over the course of the Covid-19 pandemic

2022· article· en· W4226205069 on OpenAlex
Laura Schmahmann, Ate Poorthuis, Karen Chapple

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

VenueCambridge Journal of Regions Economy and Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMetropolitan areaPandemicEconomic geographyCoronavirus disease 2019 (COVID-19)DowntownGeographyNeighbourhood (mathematics)PolycentricityEconomies of agglomerationWork (physics)Regional scienceEconomic growthBusinessEconomicsInfectious disease (medical specialty)FinanceEngineering

Abstract

fetched live from OpenAlex

Abstract The expectation of a mass movement out of cities due to the rise of remote work associated with the Covid-19 pandemic, is counter to longstanding theories of the benefits of agglomeration economies. It suggests centrifugal shifts of economic activity which could boost neighbourhood economies at the expense of the downtown core. Using mobile phone data from SafeGraph, we track migration and daily mobility patterns throughout the New York metropolitan area between July 2019 and June 2021. We find that diverse suburban centres and exurban areas have bounced back more quickly than the dense specialised commercial districts in and around Manhattan.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.315
Teacher spread0.270 · 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