Aligning the social sciences with the deep ocean: developments and definitions for a new research agenda
Bibliographic record
Abstract
The deep ocean is connected to people, communities and societies in fundamental and intricate ways, including through regulation of Earth system processes such as the climate but also through direct impacts on economies and wellbeing through resource extraction, distribution of benefits, research practices and governance regimes. While a dedicated community of natural scientists study the nature and particularities of the deep ocean, corresponding deep-sea social sciences are yet to be developed – or named as such. Economists, historians, anthropologists, law scholars and other social scientists have so far worked with different categorisations of the marine realm, focusing on political and legal classifications, such as territorial or coastal waters, exclusive economic zones, and the high seas. However, this horizontal approach to ocean zones reflects a lack of appreciation of the importance of the verticality and three-dimensionality of the ocean space, where the deeper parts of the ocean are fundamentally different from the shallower parts, regardless of whether they are in national or international territories.This paper aims to challenge and revise the traditional horizontal conceptualisation of maritime zones in the social sciences by approaching the deep ocean as a relevant category for studies of human-ocean relationships and investigating the unique social science-dimensions of deep-sea environments. Our intention is to provide a fundamental perspective of how the social sciences may interact with deep-sea definitions developed in the natural sciences over the past couple of decades. Establishing this base, we contend, will provide a sound starting point for identification and investigations of more particular social science concerns and areas of study, such as social, economic and equity impacts from specific deep-sea activities including seabed mining, marine carbon dioxide removal and storage, bioprospecting, pollution, and corresponding governance responses.Our paper draws on a collaboration between social scientists and deep-sea natural scientists, and is an attempt to bridge these fields. The collaborative effort to identify the scientific foundations for treating the deep ocean as a distinct unit of analysis includes a 2024 workshop organised by the Deep Ocean Stewardship Initiative at Scripps Institution of Oceanography, followed by continued efforts to combine diverse perspectives in multiple papers. The study presented here is the main attempt within this broader framework to align social science issues and areas of relevance to the key bio-geo-physical processes, ecosystems and ecological conditions, and challenges and threats that justify the distinct scientific treatment of the deep ocean across disciplines.
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 | 0.055 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.008 | 0.088 |
| Scholarly communication | 0.027 | 0.090 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".