The use of technological innovation in bio-based industries to foster growth in the bioeconomy: a South African perspective
Bibliographic record
Abstract
Several countries around the world are taking advantage of emerging technologies to leverage the use of natural resources to develop and grow bio-based industries. As a result, these activities have become the backbone of bioeconomy-growth strategies in the developing world. Adoption of the concepts and technological aspects of this facet of the Fourth Industrial Revolution (4IR) across government, academia, and industry has fostered innovation in the health, agricultural, and manufacturing sectors. However, the relationship between the technological catalysis of innovation and the bioeconomy from the perspective of a developing country has been left unexplored. In this context, this review explores the contribution of technological advances toward a sustainable, valuable bioeconomy and the current policy mandates. We focus our attention on South Africa because the country has a holistic, well-defined bioeconomy strategy that is consistent with the conditions of developed nations more generally. The review suggests that developing countries could adopt a multidisciplinary approach to designing their bioeconomy strategies. We further assert that developing holistic strategies that address the recent COVID-19 pandemic and potential future world crises could be beneficial in achieving sustainable development goals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".