Bibliographic record
Abstract
Wikipedia edit-a-thons represent a unique and fruitful avenue for galleries, libraries, archives and museums (GLAMs) to engage with new and existing segments of their user communities. Encompassing information and technical literacy skills development, the success of edit-a-thon campaigns, such the annual art+feminism events, demonstrate their utility and value as outreach initiatives. What is less clear are the logistical, ethical and professional development implications of hosting these events. Centering first-hand experience and concrete examples, this paper explores GLAM-based edit-a-thons through a practical and actionable lens. Topics covered include: what is required to successfully host an edit-a-thon; the importance of leading by example in a volunteer-reliant economy; and the transferable work skills gained by hosting and participating in edit-a-thons. Des « édit-a-thons » (marathon d’édition de texte) sur Wikipédia offrent aux galeries, bibliothèques, archives et musées une façon unique et efficace d’interagir avec de nouveaux ou d’existants segments de leur communauté. En incorporant le développement de compétences techniques et informationnelles, le succès des édit-a-thons, tel que l’événement annuel art+feminism, démontre leur utilité et leur valeur comme initiative de sensibilisation. Toutefois, les implications liées à la logistique, l’éthique et au développement professionnel en tant qu’hôte de tels événements demeurent ambiguës. En s’inspirant d’une expérience unique et d’exemples concrets, cet article utilise une approche pratique et appliquée pour explorer des édit-a-thons au sein des galeries, bibliothèques, archives et musées. Les sujets abordés comprennent les éléments requis pour que les édit-a-thons connaissent un succès, l’importance d’être un exemple à suivre au sein d’une économie basée sur des bénévoles et les habiletés transférables acquises en étant hôte et en participant à des édit-a-thons.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.015 | 0.038 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".