Stocktaking of the Housing Sector in Sub-Saharan Africa : Challenges and Opportunities
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
Africa is rapidly urbanizing and will \n lead the world’s urban growth in the coming decades. \n Currently, Africa is the least‐urbanized continent, \n accommodating 11.3 percent of the world’s urban population, \n and the Sub‐Saharan region is the continent’s \n least‐urbanized area. However, the region’s cities are \n expanding rapidly, by 2050; Africa’s urban population is \n projected to reach 1.2 billion, with an urbanization rate of \n 58 percent (UN‐HABITAT 2014). With this rate of growth, \n Africa will overtake Asia as the world’s most rapidly \n urbanizing region by 2025 (UN 2014). Although the nature and \n pace of urbanization varies among countries, with over a \n quarter of the world’s fastest growing cities, Africa is \n undergoing a massive urban transition. Globally, cities are \n major drivers of economic growth, and the quality and \n location of housing has long-term consequences for inclusive \n growth. However, in Sub-Saharan Africa, urbanization is not \n accompanied by the level of per-capita economic growth or \n housing investment that is observed elsewhere in global \n trends. Incomes in Sub‐Saharan Africa (SSA) have not kept \n pace with urbanization, which, in many African countries, \n has not necessarily been accompanied by industrial growth \n and the structural transformation as has occurred in other \n regions. Housing stocks, along with investment and \n employment in related construction and finance industries, \n constitute a major component of national economic wealth. \n The key challenge for African cities, however, has been the \n comparatively low growth in per‐capita income, which limits \n the resources that households have to consume or invest in \n housing. At the same time across the region, the formal \n channels through which quality housing is produced and \n financed face major constraints that limit access to a large \n share of urban households. Hence, the formal housing sector \n is only a small part of the economy because the construction \n and finance services have very little effective demand, \n evidenced by the lack of formal investment in housing across \n the region. Recent studies have found that in Africa, formal \n housing investment (in national current accounts data) lags \n behind urbanization by nine years (Dasgupta et al. 2014). \n Furthermore, the capital investment in infrastructure needed \n to handle rapid urbanization typically happens (if at all) \n after housing has already been built, often in informal 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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