How are encyclopedias cited in academic research? Wikipedia, Britannica, Baidu Baike, and Scholarpedia
Bibliographic record
Abstract
Encyclopedias are sometimes cited by scholarly publications, despite concerns about their credibility as sources for academic information. This study investigates trends from 2002 to 2020 in citing two crowdsourced and two expert-based encyclopedias to investigate whether they fit differently into the research landscape: Wikipedia, Britannica, Baidu Baike, and Scholarpedia. This is the first systematic comparison of the uptake of four major encyclopedias within academic research. Scopus searches were used to count the number of documents citing the four encyclopedias in each year. Wikipedia was by far the most cited encyclopedia, with up to 1% of Scopus documents citing it in Computer Science. Citations to Wikipedia increased exponentially until 2010, then slowed down and started to decrease. Both the Britannica and Scholarpedia citation rates were increasing in 2020, however. Disciplinary and national differences include Britannica being popular in Arts and Humanities, Scholarpedia in Neuroscience, and Baidu Baike in Chinese-speaking countries/territories. The results confirm that encyclopedias have minor value for academic research, often for background and definitions, with the most suitable one varying between fields and countries, and with the first evidence that the popularity of crowdsourced encyclopedias may be waning.
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.042 | 0.066 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".