Bibliographic record
Abstract
Over the past two decades, the role of the non-profit, voluntary sector in the world of Western capitalist countries has been thrown into high relief. The sector has grown remarkably, expanding its activities and geographic reach. Moreover, as nation-state autonomy has eroded under the onslaught of globalisation, neoliberal policies towards welfare provision have gained momentum. Pressures to restructure the welfare state and to incorporate civil society organisations, such as foundations and non-profit institutions, into the state apparatus, have intensified. Under the guise of ‘third way’ approaches to domestic social policy that have taken firm root in many countries, voluntary sector organisations are now central actors in welfare state governance. They are also critical vehicles for service delivery and for citizenship action. Traditional social science research on non-profit organisations has grown in volume and sophistication. This scholarship has emphasised the internal organisation behaviour of non-profits, relations between boards, staff and volunteers, and the challenges that the sector faces given a changing mix of funding opportunities. Non-profit research has also become far more international in scope, with a growing number of non-profit sector studies being conducted in Eastern Europe as well as the developing world. But geographers have been leaders in the vanguard of critical scholarship on state–voluntary relations and their dynamics, and in particular have emphasised the role of the geographic context of voluntary action. Geographic research has highlighted the interdependence of the voluntary sector and government at various spatial scales, the uneven spatial patterns of non-profit sector resources, place-specificities of voluntary sector activities and activism, links between voluntarism and personal subjectivity, and the increasingly contradictory role that non-profit organisations play in politics and governance. Nonetheless, the project of articulating a geography of voluntarism has only just begun. For that reason, this book represents an important and most welcome contribution. Landscapes of voluntarism showcases the richness of recent geographic work in the United Kingdom, Canada, Australia, New Zealand and the United States. It brings together some of the discipline's most insightful scholars to consider the changing dynamics of the welfare state and its implications for non-profit groups. Chapters focus on crucial subsectors such as health, mental health, social welfare and immigration assistance.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".