Stakeholder engagement strategies assessment in expanded public works programme
Bibliographic record
Abstract
South Africa has evolved and defeated a blemished past of apartheid before 1994. Even after 27 years of democracy, South Africa has been struggling to stabilise economic markets with continued control by the white minority that marginalised the black community. The unemployment rate in the Province of the Eastern Cape in South Africa has grown from 28.5% in 1993 to 45.8% in quarter 3 of 2020 (ECSECC, 2020). Set targets have not been met since 2018, with programmes implemented not attractive nor conducive for the targeted youth and persons with disabilities. The study critically evaluated the existence and the extent of stakeholder management strategies in the Expanded Public Works Programme (EPWP) within the Eastern Cape Department of Transport and the effects of not meeting the set youth and persons with disabilities targets over the years. To gain lived experiences of beneficiaries, a case study of the household Contractor Programme was used in three districts through group semi-structured interviews with non-probability purposive sampling used to select respondents using a primary data collection instrument. Data received was analysed with themes using a descriptive analysis approach to narrate the lived experiences of participants within EPWP. Results revealed a need for improved stakeholder diversity and inclusion, communication with stakeholders, management oversight, policy guidance, monitoring and evaluation within EPWP projects.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".