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Record W2527947011 · doi:10.3138/9781442673564-013

10. The Role of Universities in Regional Development and Cluster Formation

2005· book-chapter· en· W2527947011 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2005
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCluster (spacecraft)BusinessEconomic geographyProcess managementPolitical scienceGeographyComputer scienceOperating system

Abstract

fetched live from OpenAlex

David A. Wolfe Professor of Political Science and Co-Director Program on Globalization and Regional Innovation Systems Centre for International Studies, University of Toronto Introduction As the economies of the industrial countries rapidly become more knowledge-based, universities are seen as holding the key to regional economic development and cluster formation. The OECD defines the knowledge-based economy as one in which the production, use, and distribution of knowledge and information are critical to the process of economic growth (OECD 1996). Not surprisingly, the role of the university is central to the emerging knowledge-based economy. Indeed a recent survey in The Economist suggests the conception of the knowledge-based economy “portray(s) the university not just as a creator of knowledge, a trainer of young minds and a transmitter of culture, but also as a major agent of economic growth: the knowledge factory, as it were, at the centre of the knowledge economy” (David 1997, 4).

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.989
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 science0.0000.000
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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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