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Record W4283803812 · doi:10.3389/fspor.2022.937243

Shadow Stadia and the Circular Economy

2022· article· en· W4283803812 on OpenAlexaff
Taryn Barry, Daniel S. Mason, Lisi Heise

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsSpinal Cord Injury Alberta
Fundersnot available
KeywordsShadow (psychology)Scope (computer science)StadiumLeaguePerspective (graphical)Best practiceRedevelopmentBusinessPolitical sciencePublic relationsMarketingEngineeringManagementCivil engineeringEconomicsComputer science

Abstract

fetched live from OpenAlex

Most attention on stadium or arena-anchored development projects is placed on the scope and construction of the new sports facility, while less emphasis is on the facility left behind, which we describe as shadow stadia . Some shadow stadia are repurposed for mixed use development, others are demolished but have delayed redevelopment plans, while some remain abandoned and empty for years after the professional sports team or event is no longer present in the facility. The environmental impacts of shadow stadia are not fully understood, as limited research exists on how the immediate neighborhood anchored by pre-existing venues cope in the shadows of these new development plans and the loss of a sport venue and its events. Green strategies such as the circular economy may extend the lifecycle of existing sport faciltiies. To contribute to this discussion further, this perspective article will first discuss current advances in the academic literature on the circular economy. Second, it will present a comprehensive categorization of shadow stadia globally and future opportunities on integrating circularity into best practices. By doing so, this perspective article highlights several areas of future investigation that should be considered and planned for when major league sports teams and city leaders move their team and build new facilities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.146
Teacher spread0.141 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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