Contemporary landscapes of welfare: the ‘voluntary turn’?
Bibliographic record
Abstract
Introduction Over the past two decades or so, governments in Australia, Canada, New Zealand, the UK and other Western states have sought to roll back state involvement in welfare provision. The aim has been to foster a radically pluralised social economy, with non-state actors centrally involved in the delivery of social and welfare services. As Salamon et al (1999) noted, voluntary organisations have formed a key element of this vision of pluralised welfare. The growing attention given to voluntarism, they argued, reflects the severe and ongoing fiscal pressures associated with public provision in Western states; intense doubts about the capability of national governments to deal with these matters effectively in isolation; and a breakdown in the neoliberal consensus that welfare problems can be dealt with by an encouragement of the private market. This constellation of concerns has, in part, led to the development of a ‘third way’ (Giddens, 1998), a central tenet of which is the belief that while both state and market have a legitimate role to play in the provision of social welfare, community and voluntary organisations are also critical players. Etzioni (2001) points out that a further core element of the ‘third way’ has been a desire to engage citizens in the development of responsible communities. Individuals socialised into a communitarian society, he maintains, have a moral obligation to that society and, hence, are likely to be more reasonable and productive than isolated individuals. Within this framework, voluntary organisations are seen to have an important role. Not only might they bridge the gap between state and market, but through their close connection to citizens and local communities, they also have the potential to act as sites for the development of active citizenship. So it is not just welfare reform that has increased political interest in the voluntary sector. Concern over the erosion of citizenship and a perceived need to encourage the development of social capital has further underscored the potential of voluntarism as a mechanism through which to promote a local sense of civic responsibility and engagement (Brown, 1997; Putnam, 2000). In designating 2001 as the International Year of Volunteering, for example, the United Nations (UN) highlighted an international interest in the fostering of civic engagement through volunteering (Morris, 1999).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".