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Record W4380521273 · doi:10.6000/1929-4409.2020.09.46

The Development of University Technology and Innovation Incubators to Respond to the Needs of the Modern Economy

2022· article· en· W4380521273 on OpenAlexvenueno aff
Pfano Mashau, Ziska Fields

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipGovernment (linguistics)Economic growthSustainable developmentBusinessHigher educationPlan (archaeology)Political scienceEconomics

Abstract

fetched live from OpenAlex

The growing demand for technology and innovation has created a pressing need for higher education institutions in South Africa to be involved in the development of the country. The National Development Plan 2030 challenges higher education institutions to actively participate in developing and sustaining the well-being of South Africa. The article emphasises that sustainable development can be achieved through university activities, in which university technology and innovation incubators play a role; hence the need for such incubators. Transition to a knowledge society entails the university and other knowledge institutions acting in partnership with industry and government, and even taking the lead in joint initiatives. Therefore, this article recommends that technology entrepreneurs and innovation should be developed through universities, both in South Africa and in Africa as a whole, as suggested by the triple helix theory.

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)0.013
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0080.011
Open science0.0010.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.247
Teacher spread0.207 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicUniversity-Industry-Government Innovation ModelsFrench-language works237,207