Identification of retracted publications and completeness of retraction notices in public health
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
OBJECTIVES: Retraction is intended to be a mechanism to correct the published body of knowledge when necessary due to fraudulent, fatally flawed, or ethically unacceptable publications. However, the success of this mechanism requires that retracted publications be consistently identified as such and that retraction notices contain sufficient information to understand what is being retracted and why. Our study investigated how clearly and consistently retracted publications in public health are being presented to researchers. STUDY DESIGN AND SETTING: This is a cross-sectional study, using 441 retracted research publications in the field of public health. Records were retrieved for each of these publications from 11 resources, while retraction notices were retrieved from publisher websites and full-text aggregators. The identification of the retracted status of the publication was assessed using criteria from the Committee on Publication Ethics and the National Library of Medicine. The completeness of the associated retraction notices was assessed using criteria from Committee on Publication Ethics and Retraction Watch. RESULTS: Two thousand eight hundred forty-one records for retracted publications were retrieved, of which less than half indicated that the article had been retracted. Less than 5% of publications were identified as retracted through all resources through which they were available. Within single resources, if and how retracted publications were identified varied. Retraction notices were frequently incomplete, with no notices meeting all the criteria. CONCLUSIONS: The observed inconsistencies and incomplete notices pose a threat to the integrity of scientific publishing and highlight the need to better align with existing best practices to ensure more effective and transparent dissemination of information on retractions.
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: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrityScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.104 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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