Bibliographic record
Abstract
This case study emerged from research conducted by the South African Municipal Workers’ Union (SAMWU) and the Industrial Health Research Group (IHRG) that was originally commissioned by the Municipal Services Project (coordinated by Dr David Mc Donald from Queens University in Canada) looking at the state of health and safety in the three Utility Services of the City of Cape Town, namely Electricity, Solid Waste and Sanitation. Based on photographic evidence presented in this research, the Water Dialogues South Africa decided to develop a case study investigating the outsourcing of bucket services within the City of Cape Town. This report is the culmination of desktop and primary research, primarily conducted by the case study team leader, drawing on the findings of the MSP research as relevant. Community Researchers involved in household research at five informal settlements within the City of Cape Town included Senza Kula, Mpumelelo Mhlalisi, Nombuyiselo Ngali, and Zikhona Ngesi. Four previous drafts were submitted to the Water Dialogues Working Group members for their comments and feedback, which were also considered in the drafting of this final report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.264 | 0.124 |
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".