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Record W3214219740 · doi:10.5539/jgg.v13n2p10

Requiem for the American CBD: The Impact of COVID-19 and a Dispersed Workforce

2021· article· en· W3214219740 on OpenAlexvenueno aff
Matthew J Longtin, Martin Mitchell

Bibliographic record

VenueJournal of Geography and Geology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceBusinessPublic relationsContinuanceFlexibility (engineering)Coronavirus disease 2019 (COVID-19)EntertainmentMarketingEconomic growthPolitical scienceEconomicsManagement

Abstract

fetched live from OpenAlex

Digital communication technologies have rendered the American Central Business District (CBD) obsolete, and one can expect the corporate headquarters function to be removed in varying degrees from the CBD as this decade further unfolds. The COVID-19 pandemic forced corporate America to widely embrace a dispersed workforce model. We argue that corporations will continue to embrace this model for profit driven reasons, while employees will also demand its continuance for another set of reasons: some monetarily based with others rooted in flexibility and the ability to reside anywhere. In the meantime, the public will perceive such moves as environmentally friendly and enhancing worker safety. The traditional CBDs and their adjoining sports/entertainment districts will likely face significant near-term fiscal challenges, which for some cities will be magnified by poor public policy decisions and increasing crime.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0170.001

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 head0.039
GPT teacher head0.312
Teacher spread0.273 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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