Using neighborhood observation to support public housing tenants’ empowerment
Bibliographic record
Abstract
Although public housing is often described as a negative and stigmatized environment, tenants living in such an environment can cultivate a positive sense of community, which enhances their individual and collective well-being. The present study describes the second phase of an action research, aiming to facilitate the empowerment of public housing tenants acting as peer-researchers. Following a Photovoice phase, this second phase focuses on the development and first implementation of a participatory observation method as a tool for evaluating tenants' collective environment fit. A group of nine tenants contributed to develop and later completed an observation grid. The observations were then discussed in decision-making sessions. The participatory observation method proved useful in supporting tenants in their reflection process, promoting the depiction of a nuanced portrait of their residential environment while also prioritizing capacity building. Results are currently used to inform an action phase in which tenants are taking increasingly more power. Triangulating the results from multiple sites is needed to establish more firmly the added-value of this observation method in a larger action research project. Key challenges and lessons learned are described in a reflective section, sharing experiential knowledge with researchers that consider using a similar method.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".