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Record W1965901744 · doi:10.1177/0193723501251003

The Use and Misuse of Economic Impact Analysis

2001· article· en· W1965901744 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

VenueJournal of Sport and Social Issues · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsVariance (accounting)Economic impact analysisSubsidyVariance componentsEconomic analysisEconomicsPublic economicsStatisticsClassical economicsAccounting

Abstract

fetched live from OpenAlex

Economic impact studies have been an important component of the debate surrounding the merits of subsidizing professional sports teams. However, various studies have arrived at remarkably different conclusions about the size of a sports team’s economic impact on the region in which they are located. The purpose of this study is to determine the causes of the wide variance of economic impact estimates between different studies. A meta-analysis was used on the 13 studies to determine the causes of the differing impacts. Although some of the variance is due to understandable differences in the size of the geographical region and sports team in question, differing assumptions about whether the expenditure of locals should be included accounted for a substantial variance in the impact.

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)0.001
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 score0.343
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.057
GPT teacher head0.385
Teacher spread0.327 · 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