The opioid industry document archive: New directions in research on corporate political strategy
Bibliographic record
Abstract
The opioid crisis in the United States has resulted in more than 500,000 deaths since 1999, and recent estimates suggest that attributable deaths could reach 842,000 by 2032. While heroin and synthetic products such as fentanyl now account for the majority of opioid overdoses, the prescription opioid crisis that emerged in the mid-1990s was the primary antecedent to the current situation. Recent settlements in litigation against opioid producers, suppliers and retailers has resulted in the release of almost 2.5 million previously confidential internal documents that have been made publicly accessible via the online Opioid Industry Documents Archive, a collaboration between the University of California, San Francisco and Johns Hopkins University. These corporate records provide critical insights into the operations and strategies of manufacturers and other actors in the opioid supply chain. This article describes the provenance of the opioid documents and their potential value as a research resource. It then outlines methodological approaches to their analysis, drawing on comparisons in conducting research using the Truth Tobacco Industry Documents. The Opioid Industry Documents Archive is a new and important addition to existing industry document collections that enable scrutiny and analysis of the role of corporate actors in determining health outcomes. Beyond their immediate application to researching the corporate and regulatory foundations of the current opioid crisis, the opioid document collections will contribute to a greater understanding of the commercial determinants of public health by providing means to better locate the causes of public health crises, identify politically acceptable solutions to their resolution, and inform strategies for preventing future corporate-driven epidemics.
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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 | 0.047 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.038 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.040 | 0.057 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".