Hacking History: Redressing Gender Inequities on Wikipedia Through an Editathon
Bibliographic record
Abstract
Editathons are a relatively new type of learning event, which enable participants to create or edit Wikipedia content on a particular topic. This paper explores the experiences of nine participants of an editathon at the University of Edinburgh on the topic of the Edinburgh Seven, who were the first women to attend medical school in 19th century United Kingdom. This study draws on the critical approach to learning technology to position and explore an editathon as a learning opportunity to increase participants’ critical awareness of how the Internet, open resources, and Wikipedia are shaping how we engage with information and construct knowledge. Within this, there is a particular focus on recognising persisting gender inequities and biases online. The qualitative interviews captured rich narrative learning stories, which traced the journey participants took during the editathon. Participants transformed from being online information consumers to active contributors (editors), prompting new critical understandings and an evolving sense of agency. The participants’ learning was focused in three primary areas: (1) a rewriting of history that redresses gender inequities and the championing of the female voice on Wikipedia (both as editors and subject matter); (2) the role of Wikipedia in shaping society’s access to and engagement with information, particularly information on traditionally marginalised subjects, and the interplay of the individual and the collective in developing and owning that knowledge; and (3) the positioning of traditional media in the digital age.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".