Editorial Behaviors for Biasing Wikipedia Articles
Bibliographic record
Abstract
Abstract As the vital source of information on the Web that it has long become, Wikipedia (WP) has developed detailed policies and guidelines for contributing to it as well as for resolving conflicts with a goal to ensure that all important viewpoints are represented fairly and supported by trustworthy published resources. However, WP has long been criticized for systemic biases in its content, which are often implemented and maintained through the use of these same WP policies and guidelines. The present study explores editorial behaviors of a selected editor who succeeded in biasing a set of WP articles. By selecting an editor to examine through a big‐data approach, this study can serve as an objective crosscheck for previously reported tactics for introducing and maintaining bias observed from personal experiences of disputes on WP, and could provide evidence to generate empirically grounded hypotheses concerning editorial practices indicative of bias. It was found that editorial behavior around deletion and erasure is an important strategy for implementing and maintaining bias on WP, and WP policies and guidelines are used to support and validate the silencing of other voices.
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How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".