A Scan of CDC-Authored Articles on Legal Epidemiology, 2011–2015
Bibliographic record
Abstract
Objective: The Centers for Disease Control and Prevention (CDC) conducts research on legal epidemiology, the scientific study of law as a factor in the cause, distribution, and prevention of disease. This study describes a scan of articles written by CDC staff members to characterize the frequency and key features of legal epidemiology articles and their distribution across CDC departments and divisions. Methods: CDC librarians searched an internal repository for journal articles by CDC staff published from January 1, 2011, to May 31, 2015. Researchers reviewed and coded the abstracts to produce data on key features of the articles. Results: Researchers identified 158 CDC-authored legal epidemiology articles published in 83 journals, most frequently in Preventing Chronic Disease (14 publications), Journal of Public Health Management Practice (10 publications), and Morbidity and Mortality Weekly Report (9 publications). Most articles concerned the use and impact of law as a deliberate tool of intervention. Thirteen articles addressed the legal infrastructure of public health, and 3 assessed the incidental or unintended effects of nonhealth laws. CDC-authored articles encompassed policy making, implementation, and impact. Literature reviews and studies mapping laws across multiple jurisdictions constituted one-quarter of all publications. Studies addressed laws at the international, national, state, local, and organizational levels. Conclusion: Results of the scan can be used to identify opportunities for the agency to better support research, professional development, networking, publication, and tracking of publication in this emerging field.
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 | 0.014 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.101 | 0.106 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".