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Record W2766637340 · doi:10.60082/2817-5069.3182

Can Local Actors Foster a More Inclusive and Sustainable Model of Economic Development? The Role of Business Improvement Areas in the “New” Industrial Policy

2017· article· en· W2766637340 on OpenAlexaffvenue
Matias de Dovitiis, Juan F. Gomez, Rafael Gómez

Bibliographic record

VenueOsgoode Hall law journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsOccupational Cancer Research CentreYork UniversityUniversity of Toronto
Fundersnot available
KeywordsPremiseSustainabilityBusinessBest practiceTacit knowledgeInstitutionSustainable developmentPublic economicsIndustrial organizationEconomicsKnowledge managementPolitical scienceManagement

Abstract

fetched live from OpenAlex

A growing body of evidence links differing managerial practices, specifically ones addressing environmental sustainability and the management of people, to variations in performance observed across firms and countries. Firms (and by extension jurisdictions) that invest in better environmental policies (sustainability) and that empower workers (inclusivity) tend to outperform, over the long run, those that do not. Despite the gains associated with these inclusive and sustainable management techniques, large differences in the adoption of even the most basic management practices persist. We premise this article on an institution that can lower the costs of gaining best practice knowledge and help in the successful transfer of tacit knowledge—the business improvement association (BIA). After presenting evidence of managerial best practices, we look at what BIAs are currently doing (as well as what they can do) for the small- to medium-sized, independently owned firms that constitute their membership. We go on to show that local BIA efforts, occurring as they do within large urban centres and targeting firms often neglected in national-level approaches, can do more than even ‘new’ industrial policies advocated by some policy makers to help foster a more inclusive and sustainable form of economic development.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.038
GPT teacher head0.258
Teacher spread0.220 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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