Do Focusing Events and Narratives Drive Pharma Rent-seeking: Evidence from Disease Outbreaks
Bibliographic record
Abstract
Kingdon argues that for an issue to gain agenda status, three “streams” have to come together. When these independent stream meet, a window of opportunity is created for a policy change to occur. Various factors can cause the window of opportunity to occur and one of them is a focusing event or in our case a disease outbreak (Kingdon, 1995). We hypothesize that when disease outbreaks occur pharmaceutical and related companies use this as an opportunity to seek rents. Congress passes funding when focusing events and narratives provide the opportunity to payback different pharmaceutical companies who have contributed to politicians. We use ProQuest News to track the media narratives during six different disease outbreaks and use Google Trends News to see how people interact with the narratives. We find that for five outbreaks there is support for our hypothesis, and the sixth outbreak provides a constraint on our hypothesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".