Fisheries and Oceans Canada’s Maritimes Region Ecosystem-Based Management Framework for Sustainable Management
Bibliographic record
Abstract
Fisheries and Oceans Canada’s Maritimes Region Ecosystem-Based Management (EBM) Framework version 1.0 articulates a comprehensive suite of core objectives and values based on Canadian policies and international agreements. One unique strength of this Framework comes from considering management issues and problems across Governance, Ecological, Economic and Social and Cultural Pillars and objectives. It provides a common tool for assessing the Department’s national and regional progress on legislative commitments, mandate letter priorities, and various policies that require full consideration of the four pillars of sustainability. It will be useful in developing advice that addresses the consequences of potential management scenarios, including the identification and quantification of trade-offs. It is anticipated that the EBM framework, because of its holistic nature, will provide a common basis for further development of Integrated Management, Marine Planning and Blue Economy. While it was designed primarily for use in support of management planning, it will also be useful in internal departmental program and project planning. A second unique strength of this Framework is the collaborative, participatory process through which it was developed. The collaborative development process that spanned 2019-2024 included workshops and interdisciplinary task groups that brought together Fisheries and Oceans Canada staff from multiple sectors, Indigenous participants and leading Social Science and Humanities (SSH) academics for each of the four pillars of the EBM Framework. On-going work with Indigenous organizations is exploring how to strengthen the Framework with Indigenous values and ways of knowing and whether there are common values and principles underpinning both EBM and Indigenous knowledge systems that can serve as a bridge toward co-governance. Overall, this process has expanded EBM thinking across DFO sectors and provides the foundation for EBM in DFO.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".