Evaluating gender bias in Wikipedia using document embeddings
Bibliographic record
Abstract
Wikipedia is an invaluable resource, but has been criticized for being biased in many ways. In particular, studies of Wikipedia's biographies have found that women are under-represented and portrayed in biased ways. The content of Wikipedia is produced by a complex socio-technical system, with rules and protocols that can be seen as a complex algorithm. Our overarching research problem is to evaluate whether the biases in Wikipedia are a faithful depiction of our biased society, or whether this socio-technical system creates its own biases, in a phenomenon akin to algorithmic bias. In this paper, we revisit two concepts of gender bias in Wikipedia, namely the under-representation and the biased representation of women, which correspond to distinct concepts of algorithmic bias. In order to quantify systematic differences in the representation of women we use classifiers applied to neural representations of the text. We show that a large part of the measurable difference comes from men and women being notable for reasons conforming to traditional gender roles, rather than biased representations introduced in Wikipedia.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".