References that anyone can edit: review of Wikipedia citations in peer reviewed health science literature
Bibliographic record
Abstract
OBJECTIVES: To examine indexed health science journals to evaluate the prevalence of Wikipedia citations, identify the journals that publish articles with Wikipedia citations, and determine how Wikipedia is being cited. DESIGN: Bibliometric analysis. STUDY SELECTION: Publications in the English language that included citations to Wikipedia were retrieved using the online databases Scopus and Web of Science. DATA SOURCES: To identify health science journals, results were refined using Ulrich's database, selecting for citations from journals indexed in Medline, PubMed, or Embase. Using Thomson Reuters Journal Citation Reports, 2011 impact factors were collected for all journals included in the search. DATA EXTRACTION: Resulting citations were thematically coded, and descriptive statistics were calculated. RESULTS: 1433 full text articles from 1008 journals indexed in Medline, PubMed, or Embase with 2049 Wikipedia citations were accessed. The frequency of Wikipedia citations has increased over time; most citations occurred after December 2010. More than half of the citations were coded as definitions (n = 648; 31.6%) or descriptions (n=482; 23.5%). Citations were not limited to journals with a low or no impact factor; the search found Wikipedia citations in many journals with high impact factors. CONCLUSIONS: Many publications are citing information from a tertiary source that can be edited by anyone, although permanent, evidence based sources are available. We encourage journal editors and reviewers to use caution when publishing articles that cite Wikipedia.
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 | 0.039 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.057 | 0.042 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".