A Systemic Approach for Sustainability Implementation Planning at the Local Level by SDG Target Prioritization: The Case of Quebec City
Bibliographic record
Abstract
The success of the 2030 Agenda hinges on mobilization at the local level. The localization of sustainable development goals (SDGs) and their targets involves adapting them to local contexts. This case study of Quebec City, Canada, illustrates how the use of a systemic sustainability analysis tool can help integrate SDGs in the building of a sustainable development strategy at the local level. Our approach focuses on the use of an SDG target prioritization grid (SDGT-PG) and begins with the mobilization and training of a group of officers representing various city services. We first used an original text-mining framework to evaluate SDG integration within existing strategic documents published by the city. The result provides a portrait of existing contributions to SDG targets and identifies potential synergies and trade-offs between services and existing policies. A citywide prioritization workshop was held to assess the relative importance of SDG targets for the city. Priorities were then identified by combining the importance of the targets as viewed by stakeholders, the current level of achievement of SDG targets as determined by the analysis of existing documents, and the jurisdiction and responsibilities given to Quebec City in regard to federal and provincial legislation. We identified the main focus areas and related SDG targets. Furthermore, we observed whether actions needed to be consolidated or new actions needed to be implemented. The identification of synergies and trade-offs within the city service actions provides information on the links to be made between the different municipal services and calls for partnerships with other organizations. The use of the SDGT-PG allows the vertical and horizontal integration of the SDG targets and demonstrates how participation and inclusion facilitate stakeholders’ appropriation of the applied sustainable development strategy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".