What did the scientific literature learn from internal company documents in the pharmaceutical industry: A scoping review dataset
Bibliographic record
Abstract
The objective of our scoping review is to identify all scientific papers that used internal industry documents in the pharmaceutical sector and analyze what and how the scientific literature learned about corporate influence in the pharmaceutical sector through these internal documents. The scoping review allows to explore the systematic strategies used by drug companies to influence scientific knowledge, professional practices, public policy, and public opinion for their corporate interests. When it comes to public health and healthcare research, internal company documents often serve as unique sources for evidence of corporate activities in pursuit of strategic goals. We identified 37 papers in the final results. All articles obtained most of their internal document data through legal proceedings. All 37 articles unveil dynamic ghost-management strategies that pharmaceutical corporations employ to safeguard their corporate interest. The strategies identified relate to scientific capture (n=28), professional capture (n=16), regulatory capture (n=6), media capture (n=3), market capture (n=4), technological capture (n=2), civil society capture (n=4) and others (n=2).
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 | 0.016 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".