A Systemic Tool and Process for Sustainability Assessment
Bibliographic record
Abstract
Sustainability assessment is a growing concern worldwide with United Nations’ Agenda 2030 being implemented. As sustainability refers to the consideration of environmental, social and economic issues in light of cultural, historic—retrospective and prospective—and institutional perspectives, appropriate tools are needed to ensure the complete coverage of these aspects and allow the participation of multiple stakeholders. This article presents a scientifically robust and flexible tool, developed over the last 25 years and tested in different cultural and development contexts to build a framework for sustainability assessment of policies, strategies, programs and projects in light of Agenda 2030. A selected case study conducted on a major mining project in Québec (Canada) illustrates the Sustainable Development Analytical Grid performance for sustainability assessment. This tool and process is part of the United Nations’ Sustainable Development Goals Acceleration Toolkit; it is one of the most adaptable, addresses all 17 SDGs and is fully accessible for free. Other advantages and limitations of the tool and process are discussed.
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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 | 0.078 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".