The enhancement of urban economic competitiveness: the case of Montreal
Bibliographic record
Abstract
Urban economies throughout the world are under extreme pressure to be active rather than passive. This involves anticipating and responding to the threats to existing activities and the opportunities for developing new activities following the openness of markets, deregulation of industries and dramatic changes in technology that are the hallmarks of globalisation. A proper response necessarily requires the engagement of the national level of government and the mobilisation of local actors in both the public and the private sectors. While the free market and the invisible hand can very effectively allocate resources, they generally take more time to act than is available in the high-pressured environment of inter-urban competition, and are to varying degrees affected by market imperfections. If we think back analogously to recent advances in international economic theory, there is often little to chose from between two potential suppliers of a good or two potential occupiers of a specific role in the global urban hierarchy. In this situation the prize sale goes to the one that is active while passivity leads to stagnation and marginalisation. Because of this, a coherent approach to strategic planning or to policy aimed at enhancing an urban economy’s competitiveness is an absolute necessity if resources are to be efficiently allocated toward attainment of a clearly identified objective. In this chapter we will examine two basic approaches to competitiveness enhancement, and we will then apply them to Montreal – one of North America’s most active and engaged urban economies. To begin with it is natural to ask the question – why Montreal? Las Vegas, Portland and Vancouver are getting better press coverage as North America’s most interesting urban experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".