Substance Abuse and Slow-Motion Disasters: The Case of Detroit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractIn this article, I focus on problem substance use as one outcome of an underlying, "slow-motion disaster" caused by the long-term collision between corrosive structural processes, counterproductive social policies, and vulnerable populations. Using the city of Detroit as an illustration, I offer an original conceptual model for linking the causes and cascading consequences of slow-motion disasters. This model highlights the embedded connections between structural factors, such as racial segregation and systemic unemployment, and multiple destructive outcomes, including health and crime disparities, as well as problem substance use. Finally, I conclude that sociological researchers must engage with broader publics and diverse coalitions if they are to contribute to an alternative social policy—a holistic, regional "disaster response"—that takes multiple layers of causality into account, and addresses the core of vulnerabilities that make such disasters possible. ACKNOWLEDGMENTSThis paper was first presented at the Society for the Study of Social Problems 2006 Annual Meeting in Montreal, Quebec. I would like to thank Christopher Caudill, James Gruber and Lars Bjorn for their valuable feedback on earlier drafts. Andrew Golub, Pamela Aronson, Lora Lempert, Dan Little, Kurt Metzger, Sandro Galea and Juliette Roddy also offered input at various stages, and Christina Gabrielli and Jennifer Zerweck provided assistance in proofreading the manuscript. Finally, I would like to thank the reviewers and editors at The Sociological Quarterly for their thoughtful criticism.Notes
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it