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Record W1755946505 · doi:10.5172/impp.2001.4.1-3.147

Sources of ideas and knowledge for innovatory small companies: Disaggregation of Australian and Eurostat CIS2 innovation survey data

2002· article· en· W1755946505 on OpenAlexaboutno aff
John Yencken, Murray Gillin

Bibliographic record

VenueInnovation · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
FundersSwinburne University of Technology
KeywordsPublic sectorBusinessManufacturing sectorEuropean unionProduct (mathematics)Private sectorTertiary sector of the economySurvey researchService (business)Product innovationMarketingEconomic growthEconomicsEconomyBusiness administrationInternational tradeLabour economics

Abstract

fetched live from OpenAlex

SummaryThis study explores the use and importance of university and other public sector research by business enterprises reporting product, process and (in some countries) service innovations, from OECD Innovation Surveys, the European Union, Canada and Australia. The paper tests the Eurostat CIS2 innovation survey conclusion that firms saw public sector research as important according to: whether innovations are world-first; firm size; whether the research could be used by the firm. Analyzing company responses from Eurostat, ABS and Yellow Pages innovation surveys, the paper indicates variations in importance of public sector research by manufacturing sector and firm size. The study concludes that to be effective, increased public sector research expenditure should be linked to increased private sector Rol) expenditure, particularly by SMEs.

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0110.019
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.243
GPT teacher head0.294
Teacher spread0.051 · 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 designObservational
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
Published2002
Admission routes1
Has abstractyes

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