Utilizing the SDGs to develop a blue economy capacity framework enabling a shift from an ocean economy to a blue economy in Canada
Bibliographic record
Abstract
Canada has committed to establishing a socially equitable, environmentally sustainable and economically viable blue economy but has not yet determined a sustainability standard that industry must meet to be included in this aspirational blue economy. For the blue economy to be an effective, sustainable alternative to the regular ocean economy, clear criteria for ocean business must be established to reduce the risk of blue washing. The UN Sustainable Development Goals (SDGs) provide an ideal theoretical basis from which to develop a marine sector standard for blue economy industry. Using a criteria-based approach, this work selected relevant SDG targets that can guide Canada’s ocean industry in the transition to a blue economy. Through a stepwise process, the selected targets were further contextualized to the company level resulting in a blue economy capacity assessment framework (BECF) that offers four blue economy industry aims and associated enabling mechanisms. The BECF practically links the theory of the SDGs to a desired outcome, providing a method for an ocean-based company to assess its contribution to all three dimensions of Canada’s blue economy.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".