Understanding claims-making activities about social problems : the case of homelessness
Bibliographic record
Abstract
UNDERSTANDING CLAIMS-MAKING ACTIVITIES ABOUT SOCIAL PROBLEMS: THE CASE OF HOMELESSNESS IN CANADA. Liberal Democracy proposes to combine the best of all worlds; individual freedom, economic growth, equal opportunity to achieve wealth, health and happiness. In Canada, we have experienced this liberal democracy for many decades and have witnessed the growth of the modern welfare state. Increases in prosperity and growth have been tremendous, yet we are still faced with the stark reality of poverty and the huge discrepancy between rich and poor. Nowhere is this more clearly illustrated than in the housing sector. Homelessness, the ultimate housing inequality, has not been eradicated. Instead, it continues to be a pervasive and growing phenomenon. This leads to the conclusion that Canada's welfare state has not contributed successfully to eliminating and preventing homelessness. This research examines the way society deals with social problems and their emergence. The focus is on the emergence of homelessness as a social problem. It illustrates that conventional approaches to the analysis of social problems limit actions and solutions society undertakes to resolve them. A new framework for analysis is proposed; a process oriented analysis of claims-making activities as a way of understanding social problems. This thesis documents the process of recognition of homelessness as a public policy issue in Canada. It examines the role of 'process' in the development of public policy issues because the way a society views, defines and re-defines a social problem often determines the policy response. One of the key components of understanding the public policy response to homelessness lies within the process of public recognition of homelessness as a social problem. This research contends that the way in which a problem is identified and comes to be defined and the actors involved affects the types of solutions implemented. Indeed, it is this concept of process which is crucial in the emergence, life or death of a social problem as a public issue.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.066 | 0.097 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.012 | 0.013 |
| 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".