The Big Fix: How the Pharmaceutical Industry Rips Off American Consumers
Bibliographic record
Abstract
The Big Fix opens with feisty 77 year old Melva McCuddy from Ohio struggling to find more than $6000 a year to pay for her multiple medications. We learn that she travels across the United States border to Canada, where her breast cancer drug, tamoxifen, is eight times cheaper than in her local pharmacy. Then we meet her son and grandson, both with medical troubles of their own, and discover that the family has three generations without any insurance cover for pharmaceuticals, and three generations forced to rely on handouts from their doctors. “The worst thing,” Melva told the audience at the book9s launch in Washington DC this month, “is being forced to beg doctors for free samples.”
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".