Bibliographic record
Abstract
A core objective of this book is to identify the larger contribution of socially produced risks to transforming inequalities that are not being grasped in the existing risk or inequality literatures. This book aims to achieve this task by developing a framework that can integrate different risk processes so as to highlight the systematic contribution of socially produced risks to inequality. The previous chapter identified a key set of processes related to the relational distribution of environmental risks that are already contributing to the intensification of class-based inequalities, and which will increasingly do so as the effects of climate change grow. Given the significance of the impacts of these inequalities, exploring the effects of environmental risks is clearly a crucial research objective in itself; nevertheless, to identify the larger-scale, systematic social impacts of contemporary socially produced risks, it is imperative to explore how different risk processes beyond environmental risk can be integrated into this framework of the intersections of risk, power, and inequality. To pursue this goal, this chapter further redevelops the theoretical resources of the theory of risk society so as to move beyond the backgrounding of distinct risks as the separate side-effects of distinct silos of social life and works to identify certain key similarities between the distributional logics of contemporary environmental and financial risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".