Reflecting on an impact evaluation of the Grade R programme: Method, results and policy responses
Bibliographic record
Abstract
This paper describes the expansion since 2001 of a public pre-school programme in South Africa known as ‘Grade R’, summarises the findings from an impact evaluation of the introduction of Grade R, discusses the policy recommendations flowing from the evaluation and reflects on the process of implementing the recommendations. The Grade R programme has expanded dramatically, to the point where participation is nearly universal. Although a substantial literature points to large potential benefits from pre-school educational opportunities, the impact evaluation reported on in this article demonstrated that the Grade R programme, as implemented until 2011, had a limited impact on later educational outcomes. Improving the quality of Grade R, especially in schools serving low socio-economic status communities, thus emerges as a key policy imperative. Recommended responses include professionalising Grade R teachers, providing practical in-service support, increasing access to appropriate storybooks, empowering teachers to assess the development of their learners, and improving financial record-keeping of Grade R expenditure by provincial education departments. The impact evaluation was initiated by the Department of Planning, Monitoring and Evaluation (DPME) and the Department of Basic Education (DBE), and was conducted by independent researchers. The move towards increased evaluation of key government programmes is important for shifting the focus of programme managers and policymakers towards programme outcomes rather than only programme inputs. Yet the process is not without its challenges: following a clear process to ensure the implementation of the lessons learned from such an evaluation is not necessarily straightforward.
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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 | 0.217 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".