Evaluating the Community Land Record System in Monwabisi Park Informal Settlement in the Context of Hybrid Governance and Organisational Culture
Bibliographic record
Abstract
The study examined the effectiveness of a community-operated land record system (CRS), a product of an evolutionary information system planning approach under hybrid governance arrangements in Monwabisi Park informal settlement in Cape Town. To structure the analysis, the authors adapted an analytical framework for analysing land registration effectiveness to community records systems. It serves as a tool for analysing, designing and managing similar information systems. The CRS is an element of a participatory planning and development project involving a triad: (a) community-based organisations (CBOs); (b) a non-governmental organisation (NGO), which has acted as a change agent, facilitator and resource provider; and (c) the City of Cape Town. The hybrid governance institutions comprised a set of local community and government protocols. Of further significance are the organisational cultures of the CBOs, and the NGO’s information system team differs markedly from that of most land registries. The researchers examined the CRS database and operations management, interviewed key-informants and interviewed shack residents door-to-door. The CRS was effective because residents used it and largely adhered to the associated documented community protocols to defend their tenure and to effect transactions in shacks. Further contributors were the NGO and CBOs continually managed the institutional and leadership dynamics relevant to the CRS, factors often ignored in similar 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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".