Fairness and the Politics of Resentment
Bibliographic record
Abstract
Abstract The role of the emotions in the framing of welfare policies is still relatively underexplored. This article examines the role of resentment in the construction of a particular form of ‘anti-welfare populism’ advanced by the Coalition Government in the UK after 2010. We argue that UK political parties have appropriated the discourse of fairness to promote fundamentally divisive policies which have been popular with large sections of the electorate including, paradoxically, many poorer voters. In focus group research in white working class communities in the UK undertaken just before the 2010 General Election, resentments related to perceived unfairness and loss emerged as very strong themes among our respondents. We examine such resentments in terms of an underlying ‘structure of feeling’ which fuels the reactionary populism seen in ‘anti-welfare’ discourses. These promote increasingly conditional and punitive forms of welfare in countries experiencing austerity, such as the UK, creating rivalries rather than building solidarities amongst those who ‘have little’ and drawing attention away from greater inequalities.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".