Examining the Contextual Factors Influencing Intersectoral Action for the SDGs: Insights From Canadian Federal Policy Leaders
Bibliographic record
Abstract
BACKGROUND: The interdependent and intersecting nature of the Sustainable Development Goals (SDGs) require collaboration across government sectors, and it is likely that departments with few past interactions will find themselves engaged in joint missions on SDG projects. Intersectoral action (IA) is becoming a common framework for different sectors to work together. Understanding the factors in the environment external to policy teams enacting IA is crucial for making progress on the SDGs. METHODS: Interviews [n=17] with senior public servants leading SDG work in nine departments in the federal government of Canada were conducted to elicit information about issues affecting how departments engage in IA for the SDGs. Transcripts were coded based on a set of factors identified in a background review of 20 documents related to Canada's progress on SDGs. Iterative group thematic analysis by the authors illuminated a set of domestic and global contextual factors affecting IA processes for the SDGs. RESULTS: The mechanisms for successful IA were identified as facilitative governance, leadership by a central coordinating office, supportive staff, flexible and clear reporting structures, adequate resources, and targeted skills development focused on collaboration and cross-sector learning. Factors that affect IA positively include alignment of the SDG agenda with domestic and global political priorities, and the co-occurrence of social issues such as Indigenous rights and gender equity that raise awareness of and support for related SDGs. Factors that affect IA negatively include competing conceptual frameworks for approaching shared priorities, lack of capacity for "big picture" thinking among bureaucratic staff, and global disruptions that shift national priorities away from the SDGs. CONCLUSION: IA is becoming a normal way of working on problems that cross otherwise separate government accountabilities. The success of these collaborations can be impacted by contextual factors beyond any one department's control.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.064 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".