Ocean grabbing, terraqueous territoriality and social development
Bibliographic record
Abstract
This paper reframes the ocean-grabbing literature by moving beyond accounts where small-scale producers and communities are portrayed as only victims of states and capital. While state and corporate efforts to ‘grab’ resources require critical attention, the literature on ocean grabbing risks obscuring the multidimensional relations of less powerful agents. This paper engages access analysis to reveal complex spatial, social and political processes of inclusion/exclusion and roles of agents such as small-scale producers, trade unions, fishing communities and Indigenous people. Using the case of a circumpolar shrimp species, the paper examines how actors and interests in Canada legitimize access by asserting a form of terraqueous territoriality through claims of adjacency rights – the idea that people living on land contiguous to marine resources ought to have priority in developing these resources. Assertions of terraqueous territoriality enhance opportunities for marginalized groups to gain state endorsement of resource claims, but such assertions are contingent on other factors and progressively tenuous as the mobility and geographical distribution of marine species increases. The paper suggests that contingent ecological and social forces that influence access should receive greater analytical attention, particularly as climate change transforms spatial relations between land-based interests and mobile marine species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".