The political economy of titling in Jamaican urban informal settlements
Bibliographic record
Abstract
This qualitative study investigates how formalization of private property rights through titling affects poverty dynamics in urban informal settlements in Jamaica, a small “developing” state. It relies on a case study to explore the impact of titling on tenure security, housing investment, access to credit, and gender poverty and the factors that support or constrain titling initiatives. Given the relevance of the state and the efficacy of its institutions, this research uses a political economy approach to explain the institutional structures and power relationships that shape the environment in which titling programs are embedded. Data were collected through 12 semi-structured interviews, a questionnaire survey, and secondary sources, including Government of Jamaica publications and reports from donor agencies, which were analyzed using thematic analysis. Results suggest that titles enhanced resident’s sense of ownership of their property, facilitated credit access only for better educated and higher income earners, and that the state’s failure to consider women’s reproductive needs is a missed opportunity to help improve livelihoods. Findings also suggest, counter to neoliberal economic theories, that greater state involvement, particularly in the provision of basic services and improved institutional capacity, is necessary for titles to serve as a meaningful poverty reduction strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".