Using Case Study to Understand a Complex Housing Situation: Benefits and Challenges
Bibliographic record
Abstract
This paper focuses on the advantages and difficulties of the case study approach used to investigate a complex housing situation. This research strategy was chosen to understand the process of residential relocation imposed on 83 households of Stoneham near Quebec City, Canada, due to the enlargement of a highway. The study was sponsored by Quebec Ministry of Transportation with the general goal of assessing the psychosocial impacts of forced relocation on concerned households; it originally called for a paper-and pencil survey of all 83 households. After conducting a literature review on forced residential relocation, we soon realize that it was a very difficult experience with regard to the disruption of ‘home” and that meeting households individually was essential. Instead of a paper-and-pencil survey, we planed a qualitative survey of about half households. With no surprise, we encountered many refusals for participation as we recruited people for interviews; we succeeded in 14 cases. We thus had to turn to additional sources of information to grasp the complexity of the phenomenon under study. We namely included refusals and reasons of refusals in our analysis. We also considered the point of view of all actors involved in this process. In total, nine data sources were used of two main types: archival written documents and the oral discourse of relocated residents. Data triangulation enabled us to reconstruct the relocation process in all its complexity and to better understand the psychosocial impacts of forced residential relocation. The issue of diverging data and the difficulty to combine them in the analysis, as well as the challenge for social sciences of interpreting refusals and silence will be discussed in conclusion.
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 | 0.001 | 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) | 0.001 | 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".