Inclusivity and Expert Knowledge: An Examination of Wikipedia Talk Pages and Reference Lists
Bibliographic record
Abstract
Wikipedia relies on expertise to arbitrate rival knowledge claims (Sundin 2011). In choosing experts, Wikipedians define the boundaries of acceptable comment on any given subject. Inclusivity becomes a matter of how the boundaries of expertise are drawn. In this presentation I examine these boundaries and their implications as revealed through the talk pages produced and sources used by those Wikipedians writing articles on Philippine history.Wikipédia se base sur l’expertise pour arbitrer les assertions contradictoires (Sundin, 2011). En choisissant les experts, la communauté définit ce qui constitue un commentaire acceptable sur un sujet donné. L’inclusivité devient un concept sur lequel repose l’identification des frontières de l’expertise. Dans cette communication, j’examine ces frontières et leurs effets tels que révélés dans les pages de discussion produites et les sources utilisées par la communauté pour la rédaction d’articles sur l’histoire des Philippines.
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 | 0.009 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".