From ambiguity to action: a framework for assessing ocean-based projects in Canada’s Blue Economy
Bibliographic record
Abstract
Canada’s Blue Economy could benefit from an operational, equity-first definition that incorporates an environmentally inclusive framework. Current project assessments follow a neoliberal approach which prioritizes economic viability and leaves social equity and ecological concerns as secondary or tertiary. This Policy and Practice Review proposes an approach to Blue Economy activities by introducing the Blue Economy Development Approach (BEDA) - a structured methodology and decision sequence (Equity → Health → Wealth) to guide decisions. By making consent and equity a gate, pairing Indigenous and local knowledge with scientific indicators for ecosystem health, and verifying benefit-sharing before economic metrics, BEDA offers a clear, workable and inclusive path for Canada. To address the identified gaps, BEDA integrates diverse perspectives and cross-cutting linkages (e.g. the Sustainable Development Goals, SDGs) and functions as a systematic project-evaluation tool for municipal, provincial, and federal governance. Examples of potential applications show how principles translate to action, ensuring equitable local access to ecosystems; guiding provincial sustainable use of marine resources; and informing a comprehensive national Blue Economy strategy. Taken together, these contributions support Canada’s transition toward a sustainable Blue Economy, aligning national strategies with international commitments to equity, sustainability, and resilience.
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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 | 0.057 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.020 | 0.047 |
| Scholarly communication | 0.028 | 0.012 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".