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Record W2621217757 · doi:10.1386/tmsd.16.1.3_1

Science and innovation dynamics and policy in Scotland: The perceived impact of enhanced autonomy

2017· article· en· W2621217757 on OpenAlexaff
Michele Mastroeni, Omid Omidvar, Alessandro Rosiello, Joyce Tait, David Wield

Bibliographic record

VenueInternational Journal of Technology Management and Sustainable Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsOntario College of Art and Design
FundersEconomic and Social Research Council
KeywordsAutonomyReferendumCorporate governanceInnovation systemDynamics (music)Science parkPolitical scienceBridging (networking)Science policyBusinessRegional scienceIndustrial organizationEconomicsSociologyPublic administrationEconomic growthManagementComputer science

Abstract

fetched live from OpenAlex

Abstract The Scottish referendum of 2014 encouraged massive public debate, including on Scotland’s scientific performance and ability to harness innovation and increase global competitiveness. The science base in Scotland has traditionally been strong but has not translated well into innovation. This article uses statistical data, over 30 interviews and two workshops with business and policy leaders, to analyse key scientific and industrial innovation dynamics, using a regional innovation systems (RIS) approach. It investigates the perceived impact of increased autonomy on the dynamics of the Scottish innovation system (SIS). The article shows the weak relationship between science and innovation, and evidences the static nature of Scottish innovation policy geared to bridging a gap rather than improving the dynamics of the various elements in the innovation system. It suggests that an approach which aims to spur evolution in specific elements of the territorial governance system would strengthen Scottish innovation capabilities.

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)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.009
Scholarly communication0.0070.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.337
Teacher spread0.327 · 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 designQualitative
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

Citations5
Published2017
Admission routes1
Has abstractyes

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