Marine resource management and conservation in the Anthropocene
Bibliographic record
Abstract
SUMMARY Because the Anthropocene by definition is an epoch during which environmental change is largely anthropogenic and driven by social, economic, psychological and political forces, environmental social scientists can effectively analyse human behaviour and knowledge systems in this context. In this subject review, we summarize key ways in which the environmental social sciences can better inform fisheries management policy and practice and marine conservation in the Anthropocene. We argue that environmental social scientists are particularly well positioned to synergize research to fill the gaps between: (1) local behaviours/needs/worldviews and marine resource management and biological conservation concerns; and (2) large-scale drivers of planetary environmental change (globalization, affluence, technological change, etc.) and local cognitive, socioeconomic, cultural and historical processes that shape human behaviour in the marine environment. To illustrate this, we synthesize the roles of various environmental social science disciplines in better understanding the interaction between humans and tropical marine ecosystems in developing nations where issues arising from human–coastal interactions are particularly pronounced. We focus on: (1) the application of the environmental social sciences in marine resource management and conservation; (2) the development of ‘new’ socially equitable marine conservation; (3) repopulating the seascape; (4) incorporating multi-scale dynamics of marine social–ecological systems; and (5) envisioning the future of marine resource management and conservation for producing policies and projects for comprehensive and successful resource management and conservation in the Anthropocene.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".