WP:Catégorie is … liaison librarian contribution to local québécois LBGTQ+ content in Francophone Wikipedia
Bibliographic record
Abstract
Liaison librarians have unique roles in their institutions and in their greater communities. They often support and collaborate with professors and students in their subject areas and, of equal importance, have a special knowledge of resources available in their assigned liaison areas. This chapter will explore how a liaison librarian with subject responsibilities can contribute to closing gaps and community building through Wikipedia and the Wikimedia projects. It will describe the author’s contribution, as a liaison librarian, in addressing the Québécois LGBTQ+ gap in the Francophone Wikipedia project, WikipédiaFR. The author will also discuss how Wikipedia can be used as a tool for community building and raising awareness about the Québec LGBTQ+ culture and history gap in WikipédiaFR. The contribution events and their format, les soirées contributives, hosted at the Bibliothèque à livres ouverts du Centre Communautaire LGBTQ+ de Montréal, that sought to improve and create local LGBTQ+ Wikipedia content en français will be discussed. Additionally, the author will share thoughts on existing and not existing in Wikipedia for local LGBTQ+ communities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".