Health evidence meets politics: informing the development and evaluation of electoral platforms in Lebanon
Bibliographic record
Abstract
BACKGROUND: Whilst significant efforts have been undertaken to strengthen the role of evidence in policymaking, there is still limited work aiming to strengthen the role of evidence in electoral platforms, which are provided to inform citizens' voting decisions in democratic systems. This study aims to develop a guide targeted at political parties and candidates to support them in developing and communicating electoral platforms that are action-oriented, evidence-based and responsive to people needs. It also aims to pilot test and apply an evaluation tool to understand how political platforms are developed in Lebanon, a sectarian-based country, including main gaps, areas for improvement and use of evidence. METHODS: To develop the guide, we searched electronic databases and websites to identify documents on the development and evaluation of electoral platforms. We also mapped a sample of existing electoral platforms from democratic countries. Building on the guide, we generated a standard evaluation tool - referred to as K2Platform tool - for scoring electoral platforms. The tool was first pilot tested and then used to evaluate electoral platforms of candidates running for the 2022 Lebanese Parliamentary elections. RESULTS: We included 25 relevant articles that informed the development of the guide and evaluation tool. The guide presents the main phases and criteria involved in planning, designing and communicating electoral platforms. The K2Platform evaluation tool incorporates a set of 12 criteria and was used to evaluate 20 electoral platforms. The evaluation identified shortcomings in the electoral platforms, mainly the limited use of evidence and the absence of timelines and measurable indicators. CONCLUSIONS: The guide and K2Platform evaluation tool will make a breakthrough in how electoral platforms are designed to ensure they are transparent, action-oriented and responsive to people's needs. They provide essential criteria for political parties and other candidates to develop evidence-informed electoral platforms that can be translated into effective laws and policies. Our work supports evidence-informed policymaking and contributes to the science of knowledge translation by examining the use of evidence in informing electoral platforms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.291 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.038 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".