MétaCan
Menu
Back to cohort
Record W1976055051 · doi:10.1080/17460263.2014.931881

Building and Re-building a City through Sport: Hamilton, Ontario and the British Empire and Commonwealth Games, 1930–2003

2014· article· en· W1976055051 on OpenAlexaboutno aff
Carol Phillips, Nancy B. Bouchier

Bibliographic record

VenueSport in History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthEmpireHistoryPolitical scienceEconomic historyAdvertisingMedia studiesEconomySociologyAncient historyLawEconomicsBusiness

Abstract

fetched live from OpenAlex

Hamilton Spectator sports editor Melville Marks ‘Bobby’ Robinson was a proud British imperialist who wanted to create a new large sporting event to showcase the Empire's athletes when he helped create the British Empire Games. But when he sought financial support from Hamilton, Ontario's city council for the 1930 hosting of those Games, he used symbols that were less about the Empire and more about what was important to that city's local government officials, urban boosters, and citizens; he promised that the event would pay for itself, that it would promote the city abroad, and that it would leave Hamilton with sports facilities that would be ‘the envy of Canada’. Seventy years later another person from the Hamilton Spectator, its publisher Jagoda Pike, evoked Robinson's story when she led Hamilton's bid to once again host those games, now called the Commonwealth Games. That bid relied on themes similar to those used in the past – civic pride, unity, sport infrastructure legacy and economic development. Using urban regime theory as a conceptual framework, we argue that despite the difference in generations, the City of Hamilton has continued to use the Commonwealth Games for the same purpose – city building – and called upon high profile citizens from similar spheres of influence to further the bid.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSport in HistorySame topicSport and Mega-Event ImpactsFrench-language works237,207