Discrimination, Vulnerability, and Justice in the Face of Risk
Bibliographic record
Abstract
Recent research finds that perceived risk is closely associated with race and gender. In surveys of the American public a subset of white males stand out for their uniformly low perceptions of environmental health risks, while most nonwhite and nonmale respondents reveal higher perceived risk. Such findings have been attributed to the advantageous position of white males in American social life. This article explores the linked possibility that this demographic pattern is driven not simply by the social advantages or disadvantages embodied in race or gender, but by the subjective experience of vulnerability and by sociopolitical evaluations pertaining to environmental injustice. Indices of environmental injustice and social vulnerability were developed as part of a U.S. National Risk Survey (n= 1,192) in order to examine their effect on perceived risk. It was found that those who regarded themselves as vulnerable and supported belief statements consistent with the environmental justice thesis offered higher risk ratings across a range of hazards. Multivariate analysis indicates that our measures of vulnerability and environmental injustice predict perceived risk but do not account for all of the effects of race and gender. The article closes with a discussion of the implications of these findings for further work on vulnerability and risk, risk communication, and risk management practices generally.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".