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Record W2895319092 · doi:10.9770/jesi.2018.6.1(17)

Science and innovation policies in North African Countries: Exploring challenges and opportunities

2018· article· en· W2895319092 on OpenAlexaff
Amr Radwan

Bibliographic record

VenueJournal of Entrepreneurship and Sustainability Issues · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsSciencetech (Canada)
FundersHorizon 2020 Framework ProgrammeAcademy of Scientific Research and TechnologyEuropean Commission
KeywordsEntrepreneurshipSustainabilityEconomic growthPolitical scienceBusinessEconomicsEcology

Abstract

fetched live from OpenAlex

Effective science, technology and innovation (STI) policies and strategies reflect a country's successful contribution to scientific advancement.While the economic and geopolitical framework of many North African Countries (NACs) transformed enormously during the past decades, their relevant policies and performance were not responsive enough in adapting to these dynamics.This review is meant to highlight the current development and evolution of NAC's STI policies as well as similarities and identified common societal challenges within NACs.It focusses on the nexus approach to water, energy and food.The findings of this review suggest that the existing reform and development of the STI system in NACs require reorientation towards higher socioeconomic relevance and innovation focus accompanied by legislative measures, effective monitoring and evaluation tools as well as engagement of relevant stakeholders and the adequate leverage of sufficient strategic investments.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.285
Teacher spread0.201 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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