Advocacy Coalition by External Actors and Strategies Used to Influence the Emergence of the National Nutrition Policy in Lao PDR
Bibliographic record
Abstract
This article aims to explore the coalition of external actors and the strategies it deployed to influence the emergence of the National Nutrition Policy (NNP) in Lao People’s Democratic Republic (Lao PDR). The Advocacy Coalition Framework and the conceptual model of Effective Advocacy Strategies for Influencing Government Nutrition Policy were used to frame the data collection and their analysis. Sources of information were semi-structured interviews conducted with government and external actors, as well as all available documents on nutrition policy in Laos. The commitment of the government to achieve the Millennium Development Goals (MDGs) and to leave the Least Developed Country status created a favorable condition to support the emergence of the NNP in Laos. This context was a driving force for the building of an effective and convincing coalition of United Nations agencies able to accompany the government in redefining health priorities. Various strategies were used by the coalition to this end, including generating, disseminating, and using scientific evidence, assisting the government with a budget and technical expertise, providing decision-makers with opportunities to learn from other countries, and building relationships with the key actor. External actors can be a major force to support the emergence of a public policy in Laos, but this requires a window of opportunity like what the MDGs have been able to bring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".