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Record W2084450850 · doi:10.1177/089124240101500204

Local-Global Partnerships for High-Tech Development: Integrating Top-Down and Bottom-Up Models

2001· article· en· W2084450850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEconomic Development Quarterly · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTop-down and bottom-up designGeneral partnershipFunction (biology)Regional scienceLocal governmentState (computer science)Government (linguistics)Local DevelopmentBusinessHigh techEconomic growthEconomic geographyPolitical scienceIndustrial organizationEconomicsPublic administrationEngineeringComputer scienceGeographyFinance

Abstract

fetched live from OpenAlex

Advocates of high-tech development use conflicting top-down and bottom-up models to respond to the challenge of the increasing knowledge intensity of the global economy. The trend for policies in the United States, Canada, and Australia is to shift the emphasis from federal government and external resources to increased state and local responsibility. The competing top-down and bottom-up approaches are reviewed and then illustrated with case studies. Canada’s Technology Triangle and Australia’s Multi-Function Polis were both initiated in 1987 and then transformed in 1997. The evaluation of these case studies identifies weaknesses in the original models and calls for the integration of the two development approaches into a model of local-global partnership for high-tech development based on the building of local capacity through partnerships with local and external actors.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
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 head0.052
GPT teacher head0.297
Teacher spread0.245 · 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