Framing the human right to adequate housing: an analysis of United Nations Special Rapporteur country reports
Bibliographic record
Abstract
The United Nations Special Rapporteur (UNSR) for Housing is mandated to promote and protect the human right to housing at the international level. However, their work has received little academic attention. This paper investigates how the UNSR for Housing identifies human rights breaches and advocates for solutions across diverse contexts. We conceptualize these contributions using a framing analysis of 24 country reports published over 15 years (2007–2022). This dataset includes 12 high-income countries and 12 low- and middle-income countries, enabling a broad perspective on how housing problems and the right to housing are framed. We identify similarities across these reports that highlight internationally pervasive issues, such as the vulnerability of certain social groups to diverse housing problems, and inadequate housing conditions. We also find that some issues received greater emphasis among high-income countries, such as shortages of social housing, while in low/middle-income country reports, large-scale informal housing settlements were more commonly emphasized. Our analysis illuminates how the UNSR operationalizes the right to housing by setting international standards and promoting tangible improvements in housing conditions, both within and beyond the countries visited. We also demonstrate the utility of framing analysis as a method and conceptual approach in housing studies.
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 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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".