Alcohol policy process in Malawi: Making it happen
Bibliographic record
Abstract
Ferreira-Borges, C., Endal, D., Babor, T., Dias, S., Kachiwiya, M., & Zakeyu, N. (2014). Alcohol policy process in Malawi: Making it happen. The International Journal Of Alcohol And Drug Research, 3(3), 187 – 192. doi:http://dx.doi.org/10.7895/ijadr.v3i3.156Aims: This paper presents the recent history of alcohol-policy development in Malawi, describing changes in the policy process, initiatives to expand the involvement of relevant stakeholders, and efforts to limit the role and influence of vested commercial interests. We also note the challenges that remain for alcohol-policy formulation in Malawi.Design: We used a holistic, single case-study design to illustrate the process, using information generated from a combination of direct and indirect observations, document reviews, media analysis, and in-depth and semi-structured interviews.Findings: Alcohol policy development in Malawi reflects a complex combination of political and social processes, fraught with numerous stakeholder conflicts and political power plays. Despite the influence of the alcohol industry in the agenda-setting and consultative process, when adequately resourced and supported, civil society organizations can play an important and productive role in steering policy developments in a sound public-interest direction.Conclusions: Documenting this type of practical “natural experiment” provides an important opportunity for learning. The Malawi case study reinforces the need for more regular policy analysis of similar initiatives—in particular, in low-income developing countries—and for additional study of the alcohol-policy development process and policy implementation.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 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".