Increasing the Visibility and Influence of Canadian Nurses within the United Nations System
Bibliographic record
Abstract
Founded in 1945, the United Nations (UN) system has become the place where countries come together to discuss complex and multifaceted issues that no one country can tackle alone. Civil society continues to be an integral part of the UN system, supporting the work of various entities and providing expertise on core pillars such as development, human rights and peace and security. Some global nursing leaders have made considerable progress in increasing nursing engagement and visibility across the system; however, representation remains small. Despite a strong appetite to be involved in global public policy, there is also a need to increase awareness and knowledge of how to engage with and navigate key global organizations. Numerous opportunities exist for civil society to participate in, learn from and influence the work of the UN. This article provides Canadian nursing leaders with examples of pathways to explore to become formally affiliated with entities within the UN system.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.048 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".