Welfare to Work, Social Justice and Domination: an introduction to an Interdisciplinary Normative Perspective on Welfare Policies
Bibliographic record
Abstract
Introduction Since the 1990s, welfare states have been reforming their ‘passive’ social assistance schemes into ‘active’ systems that prioritize (re)integration in the (paid) labour market (Lodemel and Trickey, 2001). Governments no longer guarantee citizens’ financial security for life but focus on providing them with opportunities to work (Serrano Pascual and Magnusson, 2007; Lodemel and Moreira, 2014). Work-related obligations are now imposed on new groups, such as disabled people and lone parents (Stendahl et al, 2008) and have been extended beyond jobseeking activities (Dwyer, 2000; Handler 2004). For example, governments in Europe, as well as in the United States, Canada, Australia and New Zealand, have sought to ‘activate’ their unemployed citizens by requiring them to participate in mandatory programmes, often including work activities such as unpaid internships and ‘voluntary’ work. Programmes to push the recipients of social assistance towards paid employment – ‘welfare to work’ (WTW) programmes – are deemed a remedy for social exclusion, a way to make people (feel) more responsible, a stepping stone towards employment and a contribution to their empowerment. This WTW narrative has become dominant in policy discourse, not least due to broad political acceptance of its legitimizing philosophies, including communitarianism and certain strands of liberalism. The current volume is a response to these broadly shared justifications of WTW policies, which tend to neglect the power asymmetries that shape WTW institutions and relationships. The chapters in this book study alternative ways in which WTW programmes relate to questions of justice. Justice is not only approached from philosophical but also from legal and sociological perspectives. As such, this volume privileges real-world experiences over the abstractions characteristic of much liberal theory (e.g. Rawls’ idea of a well-ordered society). More generally, the authors seek to defend alternative forms of welfare legislation based on a politicalphilosophical theory sensitive to power asymmetries – the republican theory of non-domination – and the sociological and legal knowledge necessary to formulate more adequate and just law. This interdisciplinary normative approach offers different perspectives on WTW than scholars from multiple disciplines have provided thus far. First, with some exceptions (e.g. Dermine and Dumont, 2014; Paz-Fuchs, 2008), most normative approaches to WTW fail to take the legal normative perspective into account.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| 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".