Bibliographic record
Abstract
Following Ulrich Beck, I have tried to trace the part played by ‘environmental risk analysis’ in the discursive politics of globesity. Paralleling Beck’s emphasis on ecological advocacy in the environmental politics, I have set out to document the growing importance of risk sciences within the discursive politics of child empowerment through a case study of the ‘globesity pandemic’. Beck’s optimism about reflexive modernity was founded on his belief that the struggles over environmentalism were ultimately producing awareness of the unsustainability of the industrial economy. Although Beck well understood that the market economy was a complex system of risk distribution, he failed to pay attention to the complicity of citizens in risk allocation decisions through their consumption practices. What I have called lifestyle risks are produced by corporations but also consumed by citizens in the course of their daily choices. As the case of global warming illustrates, mitigating climate change depends not only on climate science but its acceptance within a democratic politics of lifestyle change. So too with the health issues flagged by globesity, which as my comparative case study has shown, depended on anxieties about media-saturated domesticity and the discursive politics galvanized by food marketing to children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.180 | 0.064 |
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".