From deprivation to criminality: Reversing the trend by harnessing the Blue Economy to address criminality at sea (CAS)
Bibliographic record
Abstract
African oceans and the resources within them contribute to coastal livelihoods and the food security of millions of African people; at the same time, these same people are most vulnerable to the adverse effects of climate change, marine pollution, illegal, unreported and unregulated fishing, piracy and armed robbery at sea, expansion of the Blue Economy sector and the overall impact of depleting oceans resources and insecurity. Understanding how they respond to their vulnerabilities could be useful for addressing maritime security threats holistically and redirecting investments, allowing for the sustainable development of the Blue Economy in a way that adheres to the conceptual definition of the concept, encompassing the improvement of economic growth while ensuring social equity and environmental conservation. This agenda-setting transdisciplinary paper combines ideas from criminology, security and development fields to explore the nexus between deprivation in coastal communities in Africa and criminality at sea and how Africa’s Blue Economy can be leveraged to reverse the trend and ensure sustainable development ashore. Grounded in the critical review of the literature and the author’s theoretical and empirical insights into the Blue Economy, ocean governance and maritime security in Africa. The paper enhances our understanding of the interplay between deprivation and criminality at sea, and how sustainable Blue Economy and maritime security can reverse this trend.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".