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Record W1015559837 · doi:10.33915/etd.4540

Diagnosing policy dynamics: The birth & evolution of the pharmaceutical subsystem

2009· dissertation· en· W1015559837 on OpenAlexaboutno aff
Katie R. Stores

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMonopolyMedical prescriptionPharmaceuticsPolitical scienceOrder (exchange)Government (linguistics)State (computer science)Public administrationPublic economicsBusinessEconomicsMedicineFinanceMarket economyPharmacology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.382
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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