Preparing for the United Nations Security Council: Canadian approaches to policy development
Bibliographic record
Abstract
In the run up to Canada’s bid for a seat on the United Nations Security Council (UNSC), Global Affairs Canada undertook thorough policy preparations to prepare for a potential term. Notwithstanding the unsuccessful vote on Canada’s candidacy, sharing our approach to policy design is worthwhile so that current and future policymakers can replicate these efforts and learn lessons from our experience. Indeed, transparency and openness are critical to the functioning of liberal, democratic institutions; seeing “how the sausage is made” can improve public perceptions of government accountability, which is critical in a time of waning trust in institutions. Given the wide breadth of issues that UNSC member states must be ready to engage on, the team designed a cross-cutting approach to policy development which ensured that stakeholders were able to provide valuable input to help shape Canadian positions. This process involved the crafting of “signature initiatives” and position papers to advance Canadian priorities in a structured and effective manner over the course of the potential UNSC term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.045 | 0.022 |
| Scholarly communication | 0.031 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".