Power and Welfare: Understanding Citizens' Encounters with State Welfare
Bibliographic record
Abstract
In the welfare provision of today, power takes both the shape of juridical sanctions and of attractive offers for self-development. When state institutions punish criminals, remove children at risk, or enforce sanctions upon welfare recipients the question of power is immediately urgent. It is less readily evident that power is at stake when institutions educate, counsel or 'empower' citizens. This book offers a framework for understanding and analyzing these complex and implicit forms of power at play in the encounters between citizens and welfare institutions. Taking as its starting point the idea that power takes many different shapes, and that different approaches to power may be necessary in the diverse contexts where citizens encounter welfare professionals, the book demonstrates how significant social theorists, spanning from Goffman to Foucault, can be used for inquiries into these encounters. Guiding the reader from their epistemological foundations to lucid 'state of the art' case examples, the book unpacks each of its six theoretical perspectives, and explains selected key concepts and explicates their potential for analysis. The final chapter discusses the usefulness of the theoretical approaches, their weaknesses and indicates some possibilities of theoretical integration. Including case studies of patients, nursing home residents, unemployed people, homeless people, and young offenders, from the USA, Denmark, France, Sweden, Canada, and Australia, Power and Welfare is designed for students and researchers of social policy, sociology, anthropology, political science, education, nursing and social work.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".