Improving public housing policies that target low-income households: The value of adding proximity to discretion
Bibliographic record
Abstract
Research on street-level bureaucrats has examined the various ways in which these professionals have implemented public policies in areas such as healthcare, education, and security, often emphasizing the role played by discretion in the implementation process. Despite its importance, the concept of street-level bureaucracy has scarcely been approached by housing studies. This study focuses on the role of street-level workers in the delivery of public housing to the lower-income population. We affirm the value of complementing street-level discretion with the concept of proximity, a premise borrowed from the microfinance literature, to increase the understanding of the interactions and relationships established between street-level workers and policy recipients during the implementation process. Such complementarity may contribute to a more accurate understanding of the housing policy implementation dynamics on the street-level and the possible adjustments to meet local needs. To explore this issue, we used a theoretical lens inspired by Goffman’s frame analysis that points to the importance of relational mechanisms that characterize the interactions between street-level workers and beneficiaries. These lenses were applied to a collective case study of Minha Casa Minha Vida-Entidades, a Brazilian subprogram in which street-level workers linked to social housing movements assume a leading role in the planning and execution of interventions. The results indicate that the combination of proximity and discretion has a positive influence on the implementation of housing policies. Our analysis shows the existence of nonprofit-oriented arrangements that may present different features and nuances at the implementation (micro) level and contribute to the (macro) debate on housing policies.
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.
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 | 0.000 | 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 | 0.000 | 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 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".