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Record W4315629387 · doi:10.26443/jiows.v6i2.142

Ocean and Human Health in the Blue Era, Indian Ocean and African Perspectives

2023· article· en· W4315629387 on OpenAlexvenueno aff
Rosabelle Boswell

Bibliographic record

VenueThe Journal of Indian Ocean World Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersNatural Environment Research CouncilNational Research FoundationSight Research UKUK Research and Innovation
KeywordsIndigenousNarrativeHuman rightsEnvironmental ethicsGeographyPolitical scienceEcologyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.273
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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