Bibliographic record
Abstract
The mental health effects of climate change and other ecological crises have become the object of intense public concern, giving rise to wide-ranging academic and popular debates over “eco-anxiety” and “climate anxiety.” This book offers the first extended sociological analysis of the phenomenon of eco-anxiety, engaging critically with the varied and ongoing claims-making activities through which it has been constructed as an urgent social problem. Arguing that a critical sociological perspective can shed new light upon the complex social and cultural influences feeding into the alleged “epidemic” of eco-anxiety, Soron adopts a constructionist approach to eco-anxiety that highlights the processes by which this putative condition has come to be framed as a crisis demanding a concerted collective response. Addressing key gaps and tensions within eco-anxiety discourse, this book addresses the ways in which expressions of distress over mounting environmental crises, and the various political claims that flow from them, are being shaped though the highly specific idiom of therapeutic culture. Without disputing the legitimacy of popular concerns over an environmentally uncertain future, Eco-Anxiety Rising brings debates over eco-anxiety and climate anxiety into an extended dialogue with the emergent literature on therapeutic culture and the sociology of social problems, seeking to contextualize and engage critically with a problem that has been discussed primarily from a psychological or medical vantage point.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".