Use of evidence in policy making in South Africa: An exploratory study of attitudes of senior government officials
Bibliographic record
Abstract
This paper outlines a 2011 study commissioned by the Presidency’s Programme to Support Pro-Poor Policy Development (PSPPD) which promotes evidence-based policy making (EBPM) in South Africa. EBPM refers to norms, initiatives and methods aimed at improving evidence-based policy in countries from which South Africa traditionally borrows public service reforms, particularly the UK and Canada. The study provides a descriptive snapshot of attitudes to evidence-use in policy making. All 54 senior government officials interviewed felt that evidence-use is too limited to ensure relevant, effective policy responses. This includes policies on which complex results depend and those with long-term and high-resource implications. Although all respondents regarded EBPM as self-evidently desirable, there were different views on practical application. Examples provided suggest that, where evidence was used, it was very often related to a borrowed international policy without a prior evidencedrivenanalysis of successes and failures or its relevance and feasibility in terms of local issuesand context. Policy makers generally know they should be making optimal use of availableevidence, but highlighted systemic barriers beyond the influence of individual managersto resolve. The study suggests that improved use of evidence throughout the policy cycle,particularly in analysing problems and needs, is a requirement for learning through evidencebased policy development. It suggests that political and administrative leadership will need to agree on norms, ways of dealing with the barriers to effective use of evidence and on the role of each throughout the policy cycle in ensuring appropriate evidence is available and used.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".