Ocean and Human Health in the Blue Era, Indian Ocean and African Perspectives
Bibliographic record
Abstract
In this article, I discuss the fact that human-ocean relations are increasingly being scrutinized, as scholars seek to frame, understand and mitigate the impacts of climate change on Earth. I propose that for the southwest Indian Ocean that scholars follow Chimamanda Adichie’s directive to critique ‘single stories’ by considering locally produced, embodied, sensorial relations of indigenous peoples and local communities (IPLCs) to the sea and coasts, as well as the role that such relations might play in shaping health. I add that such relations cultivate a ‘blue’ consciousness and a nascent ‘blue’ rights – borne of symbioses between humans and ocean. I add that these advance a holistic and integrated human and ocean health. The discussion makes the case for the re-placement of globalized paradigms of climate change and for the inclusion of locally generated narratives of human relations with the sea. I posit that a more careful analysis of coastal and oceanic (intangible cultural) heritages can reveal transmaterial, interspecies relations with the sea. Drawing on anthropological research in the Southwest Indian Ocean World (SWIOW) and then in coastal South Africa, I argue that such narratives can herald not only a notion of global blue rights, they can also herald, from the ‘periphery’, a blue era where there is deeper consideration of sustainable human relations with the sea.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".