Key principles of marine ecosystem-based management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecosystem-Based Management (EBM) has gained international popularity in recent years, but the lack of consensus on its definition has precluded the use of a universal implementation framework. The large number and variety of principles that make up EBM, and the diversity in perspectives among key management players, has impeded the practical application of EBM. Agreement on a list of the essential ingredients of EBM is vital to successful application. A frequency analysis of EBM principles was conducted to identify the Key Principles that currently define EBM, from a list of twenty-six principles extracted from a subset of the EBM theoretical/conceptual literature (covering a range of published sources across disciplines and application types). Fifteen Key Principles were identified (in descending frequency of appearance in the literature): Consider Ecosystem Connections, Appropriate Spatial & Temporal Scales, Adaptive Management, Use of Scientific Knowledge, Integrated Management, Stakeholder Involvement, Account for Dynamic Nature of Ecosystems, Ecological Integrity & Biodiversity, Sustainability, Recognise Coupled Social-Ecological Systems, Decisions reflect Societal Choice, Distinct Boundaries, Interdisciplinarity, Appropriate Monitoring, and Acknowledge Uncertainty. This paper also examines the development of EBM principles over time, leading to predictions on the directions EBM will take in the future. The frequency analysis methodology used here can be replicated to update the Key Principles of EBM in the future. Indeed, further research on potential emerging Key Principles such as ‘Consider Cumulative Impacts’, ‘Apply the Precautionary Approach’ and ‘Explicitly Acknowledge Trade Offs’ will help shape EBM and its successful application in the management of marine activities.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it