Bibliographic record
Abstract
Almost 40 years of war in Angola forced millions of people fleeing rural areas to seek a safe haven in the capital and to settle in informal slum settlements ( musseques ) on the periphery of Luanda. The new urban migrants created homes and settlements on land that they purchased in good faith but for which they could get no legal title. Now, they face eviction threats due to commercial interests and government infrastructure expansion. With a population today approaching of over six million, Luanda is Africa’s fastest growing and fifth largest city. A decade of post-war rapid economic growth, fuelled by rising commodity prices, has seen GDP per capita grow eightfold, but poverty reduction has not kept apace. The poor, representing over 50 % of the population, have benefited little from the ‘peace dividend’. The Angolan Government has promised to build one million homes country-wide before the 2012 elections and aims to eliminate much of the musseque in the process. However, the government’s urban plans remain hindered by a weak administration and little national implementation capacity. Despite the government’s assertion as the unique owner and manager of all land, there exists a thriving real-estate market for both formal (titled) and informally occupied land. Most urban residents with weak or non-existent tenure rights benefit little from increasing land values and are susceptible to being forcibly removed and increasingly obliged to occupy environmentally risky flood-prone areas. This paper presents the results of work on property markets in Luanda that permit a better understanding of the nature and economic value of land and identify the problems and potentials the market has to offer. The paper argues for a major reform in public land policy, recognising the legitimacy of common practices in land acquisition and long-term occupation in good faith. Inclusive land management, adapting to both formal and existing informal markets, can contribute to the improvement of urban settlement conditions and economic wellbeing of the poor in post-war Luanda.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".