Bridging LGBT+ Content Gaps Across Wikipedia Language Editions
Bibliographic record
Abstract
In the past several years, the Wikimedia Movement has become more aware of the lack of representation of specific communities, that is, content gaps. Next to geographical and gender-related initiatives, the LGBT+ Wikimedia community has organized to create LGBT+ content encompassing (among other topics) biographies, events, and culture. In this paper, we present a computational approach to collecting and analyzing LGBT+ articles. We selected 14 Wikipedia language editions to study the coverage of LGBT+ content in general, its visibility in the list of Featured Articles, and its overlap with the local content of the Wikipedia language editions. Results show that a considerable part of potentially LGBT+ related content exists across Wikipedia language editions; however, this relation is not evident in each language edition. In this sense, closing the LGBT+ content gap is about creating articles and making connection to the topic visible in already existing articles. We also analyze the frequency of biographies of persons with non-heterosexual sexual orientations. We find that even though they represent only a small share of all biographies, they are a bit more frequent among the Featured Articles. When taking into account all the LGBT+ biographies of the different languages, English context celebrities are the most visible. While part of the LGBT+ content is related to each language edition's local context, it tends to be less contextualized than the entire language editions. This indicates the possibility of growing LGBT+ content in each Wikipedia language edition by representing its most immediate LGBT+ local context. We propose a dashboard tool to find relevant LGBT+ articles across language editions and start bridging the gaps. Finally, we conclude this study by presenting recommendations for the next steps amongst the Wikipedia communities to fill some of these gaps.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".