Landscapes of voluntarism : new spaces of health, welfare and governance
Bibliographic record
Abstract
The appeal of voluntary action as a solution to growing welfare needs in advanced capitalist countries raises important questions about the social impacts and spatial equity of such provision. For the first time, these issues are addressed within a single book. "Landscapes of Voluntarism" explores the complex relationship between voluntary action, society and space. The book brings together a collection of new and innovative work by researchers from Australia, Canada, New Zealand and the UK - settings where issues of voluntarism and participation have become increasingly important for the development and delivery of social welfare policy. Prefaced by one of the foremost geographers in this field, it contains empirical and theoretical work from both new and well-established geographers. The chapters explore the interactions between voluntarism and a range of issues including governance, health, community action, faith, ethnicity, counseling, advocacy, and professionalisation. The book will be of interest not only to students and researchers in human geography but also to those working in social policy, sociology, health and political science. The detailed case material will also be of particular interest to practitioners working in the fields of health, governance, social welfare and social exclusion.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".