Hacking History: Redressing Gender Inequities on Wikipedia Through an Editathon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it