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Record W2490064574 · doi:10.1680/muen.2011.164.3.185

The Main Street Canada approach for small historic towns

2011· article· en· W2490064574 on OpenAlexaboutno aff
François Leblanc

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Municipal Engineer · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsBeautificationLocal communityLocal economic developmentDowntownEnvironmental planningGeographyPolitical scienceCivil engineeringEngineeringEconomic growthArchaeology

Abstract

fetched live from OpenAlex

During the nineteenth and twentieth centuries, small towns in Canada developed around a Main Street. This is where the shops, the restaurants, the hotels and the important public buildings were built. Yet, following the end of the Second World War, Canada’s small historic towns and their Main Streets began to deteriorate. Various initiatives were undertaken to revitalise them, notably the urban renewal efforts of the 1960s and the beautification schemes of the 1970s. For the most part, these efforts failed. In 1979, the Heritage Canada Foundation launched a programme called Main Street Canada and developed it on the premises that downtowns are complex entities: to prosper they must develop both economically and environmentally. The programme's strategy was based loosely upon the approach that the rival shopping centres used: open an office in the heart of the shopping district; install a coordinator who lives and works in the community; operate a programme based on the four-point approach of organisation, marketing, economic development and design improvement. This approach was incremental, emphasised widespread local participation, developed local resources and promoted local communal identity. This approach was successfully implemented in hundreds of Canadian small communities and in thousands in the USA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.958

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.186
Teacher spread0.116 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations3
Published2011
Admission routes1
Has abstractyes

Explore more

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