The Economic and Social Justification for Publicly Financed Stadia: The Case of Vancouver's BC Place Stadium
Bibliographic record
Abstract
Publicly financed Stadia, as manifest in numerous North American metropolitan cities, have always been at the centre of public debate and widely covered by the media. At one end of the debate adherents of such investments urge that stadia are an economic as well as a social catalyst in reviving a city, and at the same time have the capability to market and promote the image of a city. However, the cynics claim that this economic promise is a mere canard, or myth, and places an enormous financial and social burden on public expenditure. They are projects that are politically driven and motivated, and despite being financed by the public, are more oriented to the private sector. In my view, stadia on the whole are ineffective in fostering direct economic spin-off effects, but from a socio-cultural perspective are a key factor in producing significant intangible benefits, while enhancing the status of a city. The purpose of this article is to probe and delve into this debate and attempt to relate the broad theories to the issues revolving around BC Place Stadium in Vancouver. In conclusion a number of possible solutions and recommendations will be addressed to try to bridge the gap between proponents and critics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.011 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".