Where and how can Africa and India leverage the blue economy opportunities of the Indian Ocean region as a driver for sustainable development and partnerships?
Bibliographic record
Abstract
Through the blue economy (BE), Africa and India can attain sustainable development, transnational partnerships, and continental engagement targets, never been seen before. However, efforts on how this could be done have been pedestrian and less explored. This review and perspective paper utilizes a bibliometric analysis technique to analyze 1712 documents, systematically sourced from Scopus. Thus, this paper situates itself as one of the first scholarly pieces to comprehensively highlight strategic aspects that could advance sustainable Africa-India regional development partnership. Mixed comparative results are found in the literature. Since 2012, research on the BE in Africa and India has increased. The BE is emphasized as a critical topical issue in Africa, albeit this is mostly led by non-African scholars and institutions. In India, most BE research perspectives target regional issues, e.g., in the Indo-Pacific region. African researchers have published more in high-impact journals compared to their Indian counterparts. The annual growth rate of research on the BE in India is comparatively higher than that of Africa (8.69 to 5.49 percent, respectively). However, the average citations of research in all regions are declining. African authors have higher national and international co-authorship collaborations. Collaborations between Africa and India on the BE are few. Most country-level collaborations are with developed nations. Nevertheless, there is recognition of the increasing vulnerability of Africa's and India's coastal communities to megatrends and marine environmental threats. Most documents emphasize that the resource endowments in the Indian Ocean (IO) could mitigate maritime challenges to socioeconomic development and environmental stewardship. Five valuable findings are emphasized: (1) the BE is essential to the prospects of sustainable development, (2) inclusive and sustainable actions are needed to address coastal socioecological shifts, (3) several BE solutions are proposed but not put into practice, and (4) BE partnerships in the IO must include China and other emerging states of the Global South, and (5) the IO is paramount towards sustainable BE between Africa and India. Borrowing from the literature insights, and as a contribution to BE-led sustainable development partnerships between Africa and India, five strategic leverage points are identified and developed: socio-cultural, economic, institutional, environmental, and scientific. As the development of BE engagements and partnership is a new development arena in Africa and India, policymakers and researchers should: (a) initiate the Africa-India BE journal, b) leverage and link Africa's and India's existing BE initiatives, visions, and programs, c) reimagine Africa and India’s development connotations, d) start slowly but consistently, and e) recognize existing shared sustainability or sustainable development visions. To achieve this, the IO must be recognized as a shared natural resource that has the potential to compartmentalize and link the proposed leverage points. Thus, policymakers and researchers must work towards rejuvenating shared ties, histories, vulnerabilities, and BE visions. This can help strengthen regional partnerships, trust, and collaborations for a better and sustainable BE.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".