Advancing Clean Technology Entrepreneurship in the Middle East and North African (mena) Region: Law, Education and Policy Imperatives
Bibliographic record
Abstract
Two of the key priorities of the Arab world in the coming years are to develop and deploy clean technologies (cleantech) needed to combat the adverse effects of climate change in the region; and to diversify domestic economies to become low carbon economies with greater prospects for green jobs. However, despite broad political discussions of these policy goals, several countries in the Middle East and North African (mena) region continue to lag in terms of the level and adequacy of entrepreneurial cleantech start-up activities. For mena countries to bridge current gaps in entrepreneurial cleantech capital, entrepreneurship education and training is critical. This article investigates the ethical and contextual basis of cleantech entrepreneurship in the mena region. Focusing on clean technology businesses, given their national and global economic and environmental role in future low-carbon societies and economies, the article then investigates the principal causes of the limited development of cleantech entrepreneurship in the mena region. The Qatari example offers original insights on clean technology joint ventures, startups, and projects. The results indicate the need for mena countries to mainstream and integrate entrepreneurial education and training into national action plans and policies on low carbon development, in order to promote local capacity and awareness on cleantech entrepreneurship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".