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Record W4302394170 · doi:10.46692/9781847425423.011

The enhancement of urban economic competitiveness: the case of Montreal

2002· other· en· W4302394170 on OpenAlexaboutno aff
Peter Karl Kresl

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEconomic geographyEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

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. Why Montreal provides us with a good case study 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. Los Angeles, Toronto and Atlanta have had more spectacular growth in recent decades. But it is cities like Chicago, Cleveland, and Montreal that have had experiences which are more difficult and less assured of success, and it is this kind of urban economy that gives us our best and most informative example of strategic thinking and accomplishment. Montreal is one of the most interesting cities from the standpoint of the objective of this chapter due to the following aspects: • Its location is rather peripheral to the main areas of economic activity in North America – a bit north of the ‘industrial heartland’ – Boston to Chicago to Cincinnati to New York.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.300
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.286
Teacher spread0.266 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2002
Admission routes1
Has abstractyes

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