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Opening Science and Innovation: Opportunities for Emerging Economies

2020· article· en· W3112871687 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

VenueForesight-Russia · 2020
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsOpen innovationGeneral partnershipRelevance (law)Context (archaeology)BusinessOpen scienceSubject (documents)Knowledge managementMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Open innovation allows partnerships between business through knowledge sharing. The mission of open science is to encourage information sharing about academic research. The purpose of this paper is to demonstrate the relevance of open science to open innovation and vice versa, especially in the context of emerging economies. Furthermore, it aims to show the results of the intersection between university and innovation companies. The methodology was based on a systematic literature review to understand how researchers have been studying the subject. It also focuses upon the relevance of open innovation and open science to the business management and information science fields. Therefore, the connection between open science and open innovation is fundamental to encouraging partnerships between businesses and universities. This kind of partnership contributes to the economy of developing countries, so business can become more competitive.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.104
GPT teacher head0.280
Teacher spread0.177 · 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