Can community land trust models work in Peru? Researching community-based land tenure models for affordable housing
Bibliographic record
Abstract
Purpose In pursuit of affordable housing, the Sustainable Urban Development Act of 2021 contains regulations for community land trusts (CLTs) in Peru. This study aims to assess whether the CLT model can be an effective tool for low-income housing generation in the Peruvian context. Design/methodology/approach This study draws upon information collected from qualitative research and official statistical data to identify the main problems in the Peruvian housing sector. The authors gathered evidence from specialised literature to examine the benefits and drawbacks of CLT implementation and functionality as experienced in the USA, England and Canada in contrast to Puerto Rico and Brazil. To assess the potential effectiveness of the CLT model in Peru, the results from the examination of both groups of countries are analysed and contrasted with the evidence from the Peruvian experience. Findings Through micro-scale interventions in places with a consolidated sense of community, the CLT model can be an effective tool for affordable housing generation. However, no robust evidence suggests that the CLT model could be an effective tool for large-scale intervention in cities with disorganised and accelerated growth. Moreover, the level of housing affordability defined by the CLT model may be insufficient for people from the lowest-income percentiles. Originality/value Peruvian CLT adaptation will require a shift in individual property mind-sets. Furthermore, the model should be enhanced by governmental support through public subsidies and backed by mortgage loans and land grant programmes.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".