Making the creative city: a case study of the quartier des spectacles in Montreal
Bibliographic record
Abstract
This research focuses on the practical applications of the theory of the creative city in urban planning in the form of creative districts, through the case study of the Quartier des Spectacles in Montreal. It is based on a series of interviews with key stakeholders in the project and on a thorough literature review. The analysis highlights the evolution of the planning process through time and its impact on creativity and urban change in the Quartier des Spectacles. The results of this study are consistent with key findings on creative districts in the literature: the transformation of the neighbourhood initiated by the planning project has produced gentrification and the displacement of lower-value uses, which results in a shift from creative production to creative consumption in the Quartier des Spectacles. The lack of control over real-estate development threatens the mere nature of the Quartier des Spectacles as a creative district. In addition, the corporate vision of culture conveyed by the project and the increased control over the uses of public space has led to an homogenization of art and culture in the neighbourhood. The case of the Quartier des Spectacles highlights the need for planning intervention to prevent real-estate speculation and maintain affordable spaces for all kinds of cultural producers in creative districts, in order to ensure the long-term capacity of these spaces as incubators for creation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".