“Always read the small print”: a case study of commercial research funding, disclosure and agreements with Coca-Cola
Bibliographic record
Abstract
Concerns about conflicts of interest in commercially funded research have generated increasing disclosure requirements, but are these enough to assess influence? Using the Coca-Cola Company as an example, we explore its research agreements to understand influence. Freedom of Information requests identified 87,013 pages of documents, including five agreements between Coca-Cola and public institutions in the United States, and Canada. We assess whether they allowed Coca-Cola to exercise control or influence. Provisions gave Coca-Cola the right to review research in advance of publication as well as control over (1) study data, (2) disclosure of results and (3) acknowledgement of Coca-Cola funding. Some agreements specified that Coca-Cola has the ultimate decision about any publication of peer-reviewed papers prior to its approval of the researchers' final report. If so desired, Coca-Cola can thus prevent publication of unfavourable research, but we found no evidence of this to date in the emails we received. The documents also reveal researchers can negotiate with funders successfully to remove restrictive clauses on their research. We recommend journals supplement funding disclosures and conflict-of-interest statements by requiring authors to attach funder agreements.
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 | 0.043 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".