Kaiser Family Foundation Poll: February 2019 Kaiser Health Tracking Poll
Bibliographic record
Abstract
Affordable Care Act (ACA), Obamacare favorability (1); favor/oppose Medicare-for-all (1); prescription drugs have made lives better/worse (2); cost of prescription drugs (1); prescription drug prices in US vs. Canada and Western Europe (2); confidence in safety of prescription drugs in US (1); trust in pharmaceutical companies (5); opinion of prescription drug regulation (2); government regulation/competition among companies would do better job at keeping drug costs down (1); favor/oppose various actions to keep drug costs down (9); federal government does/does not negotiate with drug companies for lower Medicare drug prices (2); arguments against federal government negotiating Medicare drug prices (5); factors contributing to prescription drug prices (4); health insurance coverage (5); prescription drug coverage (4); problems with health insurance plan (3); choosing prescription drug or insurance plan (3); currently take prescription medicine (7); paying for prescription drugs (3); not taking prescription as recommended because of cost (5); prescription discount (3); health condition (1); pre-existing conditions (1); chronic health conditions (1); President Donald Trump job performance (1).
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.078 |
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".