Beck’s creative challenge to class analysis: from the rejection of class to the discovery of risk-class
Bibliographic record
Abstract
Beck’s rejection of the relevance of class in Risk Society has had an immense impact on both the fields of class analysis and the sociology of risk. In outlining a novel theory of the systemic importance of risks as side-effects and in making bold claims about how the production and distribution of risks are undermining class inequalities, Beck posed a highly influential challenge to both risk and class studies. Beck’s impact on class analysis however has not been mainly due to widespread acceptance of his original claims about risk and class; rather, it has been research building upon Beck’s work so as to critically depart from his conclusions through which Beck has made his contributions to the study of class. In seeking to identify the impact of Beck’s work on the study of risk and class, this paper, firstly, outlines Beck’s challenge to class analysis. It then proceeds to identify three key areas of research whose development was motivated by their critical engagement with Beck’s work: the literature on risk and the continuity of class; the critical theory of the individualization of class inequality; and the political economy of risk-class. This paper then concludes by critically evaluating Beck’s more recent, partial acknowledgement of risk inequalities by arguing that there are significant limitations in his account of class, but that his work continues to offer a valuable opportunity to inspire future work on class and inequality.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.072 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".