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Record W4256177213 · doi:10.1504/ijtm.2018.093939

A glance at research-driven university's technology transfer office in the UAE

2018· article· en· W4256177213 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Technology Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsOntario Tech University
FundersUtah Agricultural Experiment Station
KeywordsTechnology transferBusinessProsperityInnovation managementIndustrial organizationMarketingEconomicsEconomic growthInternational trade

Abstract

fetched live from OpenAlex

Technology transfer offices (TTOs) in research-driven universities serve as an intermediary between suppliers of innovations and those who can potentially commercialise them. In the United Arab Emirates (UAE), TTOs are expected to take an important role in the evolution of successful spin-off companies from innovation to production to sales to sustainable profit. TTOs aim to help businesses to innovate and prosper leading to improving local and national economic prosperity. TTOs often support spin-off companies becoming a learning organisation and easing into an articulated management of activities complementary to the research and development activities that create the innovation and drive the transition from innovation to product lines. This paper aims to investigate the current situation of research-driven university's TTO in UAE using a case study of Etisalat BT Innovation Center at Khalifa University and Masdar Institute for university policy implications. The findings suggest that TTOs assist university researchers in many ways.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.033
GPT teacher head0.354
Teacher spread0.321 · 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