A transdisciplinary co‐conceptualisation of marine identity
Bibliographic record
Abstract
Abstract Challenge 10 of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030) calls for the restoration of society's relationship with the ocean. Research suggests that the relationship people have with marine environments can influence their depth of engagement in marine citizenship action, and the important role for ‘marine identity’ in driving that action. Although identity is well‐researched, marine identity is a concept novel to academia and a baseline understanding is required, both to grasp the scope of the concept, and to support research into its role in transforming the human‐ocean relationship. Here, a transdisciplinary study, endorsed as a UN Ocean Decade Activity and by the EU Mission Ocean & Waters, brought together a multinational community of marine researchers and practitioners to co‐produce a baseline conceptualisation of marine identity, drawing on photovoice and deliberative methodology. This paper presents the findings of the co‐production process and offers a first introduction in the literature of the multiple variations and formations of marine identity. We find marine identity to be a complex and multidimensional concept, suffused with individual experiences and understandings of the marine environment, based on social and cultural understandings of the ocean, contemporarily and historically. We present real‐world examples of marine identity to illustrate key themes that were developed through co‐production. Policy implications: We propose marine identity as a catalyst for understanding existing multifaceted and caring relationships with the ocean, as well as the restoration of society's relationship with the ocean. Marine identity research should, therefore, be prioritised in research seeking to contribute to the UN Ocean Decade Challenge 10, as this will support integration of non‐material values of the ocean into marine planning processes and policy making, enabling effective responses to Challenge 10's emphasis on integrating traditional/cultural ways of knowing and valuing the marine environment, through diverse marine identities. We welcome research efforts that will further develop the marine identity concept and empirically investigate the relationships between marine identity, marine citizenship, and people's relationships with the ocean. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 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".