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Record W7000005955

Economic Impact of the Michigan Large Special Event Fund

2018· article· en· W7000005955 on OpenAlexaboutno aff

Bibliographic record

VenueUpjohn Research (W.E. Upjohn Institute for Employment Research) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic impact analysisRevenueConventionWork (physics)State (computer science)Visitor patternAuthorizationScope (computer science)CommissionQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

The Regional Economic Planning Strategies team will assist the Detroit Metro Convention and Visitors Bureau (DMCVB) to better understand the implications that the Michigan Large Special Fund (MLSP) is having on both the Detroit region and the state of Michigan. The intent of this analysis is to help the DMCVB to secure what is currently an annualized authorization of the Fund as a more permanent component in the state of Michigan's budget. This fund supports the attraction efforts of convention and visitor bureaus across the stat in attracting large (4,000+) conventions that will attract a significant share of visitors from outside of Michigan. This work will estimate the impact on employment and some public sector revenues based on forecasts of number of visitors and their time in the state and associated spending patterns. These estimates will be created using the REMI model.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.249
GPT teacher head0.532
Teacher spread0.284 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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