Bibliographic record
Abstract
Wikipedia articles are the result of the collaborative editing of a diverse group of anonymous volunteer editors, who are passionate and knowledgeable about specific topics. One can argue that this plurality of perspectives leads to broader coverage of the topic, thus benefitting the reader. On the other hand, differences among editors on polarizing topics can lead to controversial or questionable content, where facts and arguments are presented and discussed to support a particular point of view. Controversial articles are manually tagged by Wikipedia editors, and span many interesting and popular topics, such as religion, history, and politics, to name a few. Recent works have been proposed on automatically identifying controversy within unmarked articles. However, to date, no systematic comparison of these efforts has been made. This is in part because the various methods are evaluated using different criteria and on different sets of articles by different authors, making it hard for anyone to verify the efficacy and compare all alternatives. We provide a first attempt at bridging this gap. We compare five different methods for modelling and identifying controversy, and discuss some of the unique difficulties and opportunities inherent to the way Wikipedia is produced.
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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".