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Record W4411067186 · doi:10.17645/oas.9797

Ocean Literacy for Ocean Sustainability: Reflections From Australia

2025· article· en· W4411067186 on OpenAlexaff
Rachel Kelly, Prue Francis, Rebecca Shellock, Stefan Andrews, Benjamin Arthur, Charlotte A. Birkmanis, Harry Breidahl, Lucy Buxton, Jocelyn Chambers, Emma Kowhai Church, Corrine M. Condie, Freya Croft, Cátia Freitas, S. J. Hurley, Emily Jateff, Brianna Le Busque, Justin D. Marshall, Allyson L. O’Brien, GT Pecl, Laura Torre-Williams, Sophia Volzke, Yolanda Waters

Bibliographic record

VenueOcean and Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDepartment of Environment and Conservation
FundersAustralian Marine Sciences Association
KeywordsSustainabilityLiteracyOceanographyEnvironmental scienceGeographySociologyGeologyEcologyPedagogyBiology

Abstract

fetched live from OpenAlex

Ensuring a sustainable future for the global ocean requires meaningful dialogue and engagement with society. Around the world, efforts to engage and collaborate with society increasingly emphasise ocean literacy as a potential tool for engaging and educating people on ocean issues. A conceptual measure of people’s awareness, attitudes, and behaviours towards the ocean, ocean literacy has been highlighted as a key objective in recent ocean sustainability agreements and initiatives, including the UN Decade of Ocean Science for Sustainable Development. In Australia, research and applied interest in ocean literacy is burgeoning. It is therefore timely to take stock and explore recent work that may inform future pathways towards supporting and engaging society in achieving ocean sustainability. Here, we explore examples of ocean literacy research and practice in Australia, to develop prospective thinking on inter/transdisciplinary approaches for advancing ocean literacy under sustainability objectives. In doing so, we anticipate the next steps for progressing ocean literacy in the Australian context, including supporting ocean learning and education, engaging communities at all levels, fostering cross‐sector collaboration on connecting people to the ocean, and building strong and actionable policy and funding frameworks to ensure long‐term impact. We emphasise the need to collaboratively develop a national ocean literacy strategy to guide and structure these efforts and to establish an Australian ocean literacy coalition to facilitate research, cross‐sector collaboration, and implementation in practice.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.302
Teacher spread0.287 · 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 designNot applicable
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

Citations6
Published2025
Admission routes1
Has abstractyes

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