The Opioid/Overdose Crisis as a Dialectics of Pain, Despair, and One-Sided Struggle
Bibliographic record
Abstract
The opioid/overdose crisis in the United States and Canada has claimed hundreds of thousands of lives and has become a major field for research and interventions. It has embroiled pharmaceutical companies in lawsuits and possible bankruptcy filings. Effective interventions and policies toward this and future drug-related outbreaks may be improved by understanding the sociostructural roots of this outbreak. Much of the literature on roots of the opioid/overdose outbreak focuses on (1) the actions of pharmaceutical companies in inappropriately promoting the use of prescription opioids; (2) "deaths of despair" based on the deindustrialization of much of rural and urban Canada and the United States, and on the related marginalization and demoralization of those facing lifetimes of joblessness or precarious employment in poorly paid, often dangerous work; and (3) increase in occupationally-induced pain and injuries in the population. All three of these roots of the crisis-pharmaceutical misconduct and unethical marketing practices, despair based on deindustrialization and increased occupational pain-can be traced back, in part, to what has been called the "one-sided class war" that became prominent in the 1970s, became institutionalized as neo-liberalism in and since the 1980s, and may now be beginning to be challenged. We describe this one-sided class war, and how processes it sparked enabled pharmaceutical corporations in their misconduct, nurtured individualistic ideologies that fed into despair and drug use, weakened institutions that created social support in communities, and reduced barriers against injuries and other occupational pain at workplaces by reducing unionization, weakening surviving unions, and weakening the enforcement of rules about workplace safety and health. We then briefly discuss the implications of this analysis for programs and policies to mitigate or reverse the opioid/overdose outbreak.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".