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Innovation Highways and the Geography of Inclusive Growth

2018· book· en· W2791299371 on OpenAlexaff
Anita M. McGahan, Janice Gross Stein

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProsperityScholarshipCorporate governanceEconomic geographyHuman geographyPolitical scienceResource (disambiguation)Innovation economicsRegional scienceGeographyEconomic growthEconomic systemEconomicsManagement

Abstract

fetched live from OpenAlex

Important advances regarding the geography of innovation focus on the competitiveness of cities, nations, and regions through the establishment of innovation clusters and national systems of innovation. In this chapter, this logic is linked with emerging scholarship on innovation for inclusive growth, which focuses on entrepreneurialism in resource-limited settings. By connecting the two streams, the chapter conceptualizes relationships between communities as ‘innovation highways’. It is argued that economic and public policy seeking to advance both prosperity and inclusiveness would benefit from deeper and more extensive consideration of collaboration between communities. The chapter argues that future research on the geography of innovation will take innovation highways between communities as central to prosperity, and consider the governance of these highways as a central mechanism of inclusiveness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.013
Scholarly communication0.0070.009
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.010
GPT teacher head0.178
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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