Diagnosing policy dynamics: The birth & evolution of the pharmaceutical subsystem
Bibliographic record
Abstract
The rising cost of prescription drugs in the United States has led patients---older populations and the disabled especially, to seek relief through foreign nations, and internet mail-order sites, which are often hosted and condoned by state and local governments. Patients are traveling to Canada and Mexico to purchase affordable prescription drugs. According to the Congressional Budget Office, "American seniors alone will spend 1.8 trillion dollars on pharmaceuticals over the next ten years" (U.S. Senate 2007, S 251). This research examines the agenda status and change of pharmaceutical regulation by tracing the evolution of the pharmaceutics subsystem. By employing a punctuated equilibrium approach, I seek to understand if periods of agenda access and issue definition have corresponded to changes in the institutional structure of policymaking.;As such, this study is motivated by three questions: (1) how has Congress governed the pharmaceutical policy agenda over the post World War II era, (2) have periods of agenda access led to venue changes in pharmaceutical regulation, (3) has the image of pharmaceutical policies led to positive or negative feedback, and if so, what factors precipitated such change. Understanding how image and agenda access can impact the institutional structure of policymaking will illustrate how ideas influence the strength and weakness of the pharmaceutical policy monopoly. The results of this study are important because they highlight the institutional factors influencing the cost and availability of prescription drugs. Moreover, this research provides insight concerning federal involvement in regulatory policy.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".