Welfare is ‘bad’: bringing it all together
Bibliographic record
Abstract
This chapter sets out to encapsulate and affirm all of the empirical materials that have been presented so far in a way that draws together all the different and complex strands. For this reason, it revisits much of what has been recounted already, including some of the most pertinent data excerpts. The aim of this chapter is to suggest that the contemporary ‘welfare imaginary’ has shifted irrevocably, particularly in the time since the embedding of the post-war welfare commons, and to show that, because of this, there is a persistent and pervasive tendency within the liberal welfare regimes of the Anglosphere for welfare provision – a social good originally imagined as something positive and necessary – to be thought of as inherently ‘bad’. I suggest that the contemporary ‘welfare imaginary’ is badly damaged and that the sociology of this drives lived experiences in the context of welfare recipiency. This break or schism in the ‘welfare imaginary’ is something that has been addressed elsewhere. Jensen and Tyler (2015: 471), themselves drawing on a range of sources, note this in the context of the UK in a way that is worth recounting: It is difficult to remember from a contemporary perspective that the Keynesian welfare state was imagined by its original architects as a ‘cradle to grave’ safety-net for citizens: a ‘welfare commons’ of ‘shared risks’ which would function to ameliorate economic and social hardships, injustices and inequalities (see Timmins, 2001; Lowe, 2005; Glennerster, 2007). The landmark publication of the Beveridge Report in 1942 saw people queuing outside government offices in their desire to get their hands on a copy of this blueprint for a new welfare state (Page, 2007: 11) and the report sold over 100,000 copies within a month of its publication. More recently, Fitzpatrick and colleagues (2019) return to T.H. Marshall's idea of a basic minimum standard of social and economic security as a facet of social rights. Drawing on Hoxsey (2011), and with recent reforms in the UK in mind, they make the following observation: These developments beg the question whether social citizenship is entering a post-Marshallian phase, or returning to a pre-Marshallian form; even whether, as Hoxsey suggests of Canada, citizenship is taking on an individualistic and marketised form shorn of its social element. (Fitzpatrick et al, 2019: 5)
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".