Aid and Health 1 Globalization and Health Knowledge Network: Research Papers WHO Commission on Social Determinants of Health Aid and Health Globalization Knowledge Network
Bibliographic record
Abstract
The Globalization Knowledge Network (GKN) was formed in 2005 with the purpose of examining how contemporary globalization was infl uencing social determinants of health. It was one of nine Knowledge Networks providing evidence-informed guidance to the work of the World Health Organization’s Commission on Social Determinants of Health (2005-2008): like most of the Knowledge Networks, its operations were fi nanced by an external funder (in this case, the International Affairs Directorate of Health Canada, Canada’s national ministry of health). The GKN conducted two face-to-face meetings to debate, discuss, outline and review its work, and produced thirteen background papers and a Final Report. These papers and the Final Report underwent extensive internal and external peer review to ensure that their fi ndings and policy inferences accurately refl ected available evidence and scholarship. This GKN publication series was prepared under the general editorship of Ronald Labonté, with assistance from Vivien Runnels and copy-editing provided by Wayne Harding. All views expressed are exclusively those of the authors. A complete list of titles in the publication series appears on the inside back cover of this monograph.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".