Development of Framework for Improved Sustainability in the Canadian Port Sector
Bibliographic record
Abstract
Canada Port Authorities (CPAs) are federal entities responsible for managing Canadian Ports with local, national, and international strategic importance. Despite their connection to the Government of Canada, the CPAs inconsistently report sustainability performance and are absent from Canada’s Federal Sustainable Development Strategy (FSDS)—a national strategy to operationalize the United Nation’s (UN) Sustainable Development Goals (SDGs). Sustainability initiatives currently used by CPAs only contribute towards attaining 14 of 36 relevant SDG targets, suggesting the need for an additional sustainability framework to achieve the remainder of these targets. This paper proposes a port-specific framework based on disclosures from the Global Reporting Initiative (GRI) to fill performance gaps in current sustainability initiatives. Disclosures were selected in an iterative process based on literature and industry best practices. The framework provides a unified approach for both CPAs and policymakers to attain SDG targets relevant to the Canadian port sector and align sustainability performance with Canada’s FSDS.
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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 | 0.054 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".