The City as an Image-creation Machine: A Critical Analysis of Vancouver's Olympic Bid
Bibliographic record
Abstract
The strategic mobilization of images, visual metaphors, and other forms of graphical rhetoric has always been central in place promotion. Images of place have assumed even greater importance, however, with the rise of locational tournaments of cities bidding for the "right" to host high-stakes transnational spectacles. In this paper, we adapt Harvey Molotch's pioneering theory of the urban growth machine to illuminate the contemporary enterprise of city bids for the Olympic Games. Taking Vancouver's successful bid for the 2010 Winter Games as a case study, we use a visual methodology framework to analyze the manifest (explicit, surface) and latent (implicit, subtle) visual narrative strategies used to craft a carefully considered representation of the city. Our analysis of the official Bid Questionnaire and the video presentation to the International Olympic Committee documents the sophisticated process by which a city is constructed to embody pristine urban nature, multicultural social harmony, and vibrant local cultures of sport in keeping with the spirit of Olympism. Whether imagined cities like this are effective is irrelevant: cities understand that half of their advertising budget is wasted (they just don't know which half). The expanding symbolic economies of tourism, conventions, and hallmark events require that urban growth machines develop and operate a full suite of image creation machines, each attuned to the real and perceived desires of an elusive transnational audience in a perpetual movable feast of locational consumption. [End Page 24]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".