Bibliographic record
Abstract
Purpose The purpose of this paper is to report on a research study that entailed the rigorous evaluation of the quality of a large multidisciplinary sample of Wikipedia articles. The objective of the paper is to assess whether Wikipedia can be used and recommended as a credible reference or information tool. Design/methodology/approach The 106 randomly generated Wikipedia articles are analyzed and evaluated on specific criteria (completeness, accuracy, presentation, objectivity, and overall quality). Articles are reviewed from a broad range of subject areas: arts, popular culture, entertainment, geography, history, science, technology, people, entities, and politics. Findings The findings indicate that overall the articles are objective, clearly presented, reasonably accurate, and complete, although some are poorly written, contain unsubstantiated information, and/or provide shallow coverage of a topic. Research limitations/implications Further research on evaluating Wikipedia entries should include reviewing outward links to more accurately assess overall quality. Practical implications Wikipedia has a role as a reference and instruction tool. Originality/value This paper provides empirical data on a large number of articles on a wide range of disciplines in Wikipedia, supporting its use as an acceptable encyclopedia.
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 | 0.032 | 0.269 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| 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".