Developing Amenities to Create More Sustainable and Inclusive Human Settlements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainable human settlements are the totality of any organised human community whether a city, town or village. This includes amenities such as parks, sports facilities, libraries, schools and clinics. Rapid urbanisation and a lack of resources in many developing countries, such as South Africa, mean that some human settlements may not have these amenities. A lack of amenities in human settlements affects the quality of life and hampers the achievement of Sustainable Development Goals (SDGs), including those for health (SDG3), education (SDG4), inequality (SDG10), and sustainable cities (SDG11). In South Africa, a lack of amenities in human settlements also affects the fulfilment of education, health and environmental rights outlined in the South African Constitution. Addressing amenity gaps must therefore be an urgent priority. This study aims to provide insight into how this can be done. In particular, it intends to contribute to the development of policy on amenities in human settlements. To achieve this objective, draft policy statements are prepared that make proposals on the type of amenities required and how these can be developed and managed. A survey of key human settlement stakeholders is used to evaluate these statements and gauge levels of support for proposals. Findings from the survey indicate that support is mixed but that there is strong overall support for the proposed amenity policy statements. These findings are drawn in making recommendations for the development of policy on amenities in human settlements. The positive findings indicate there is a strong basis for the South African government to use policy statements piloted in the study as inputs in their policy development process for amenities in human settlements.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it