Haunted Manuscripts: Ghost Authorship in the Medical Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ghost authorship occurs when an individual who contributed substantially to a manuscript is not named in the byline or acknowledgments. Ghost authors may be employed by industry to prepare clinical trial results for publication. An expert is then "hired" as author so as to lend an air of credibility and neutrality to the manuscript. Ghost authorship is difficult to detect, and most articles that have been identified as ghostwritten were revealed as such only after investigative work by lawyers, journalists, or scientists. Ghost authorship is ethically questionable in that it may be used to mask conflicts of interest with industry. As it has been demonstrated that industry sponsorship of clinical trials may be associated with outcomes favorable to industry, this is problematic. Evidence-based medicine requires that clinical decisions be based on empirical evidence published in peer-reviewed medical journals. If physicians base their decisions on dubious research data, this can have negative consequences for patients. Ghost authorship also compromises academic integrity. A "film credit" concept of authority is one solution to the problems posed by ghost authorship. Other approaches have been taken by the United Kingdom and Denmark. A solution is necessary, as the relationship between authorship and accountability must be maintained.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.034 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it