The Role of Local Content in Wikipedia: A Study on Reader and Editor Engagement
Bibliographic record
Abstract
About a quarter of each Wikipedia language edition is dedicated to representing “local content”, i.e. the corresponding cultural context (geographical places, historical events, political figures, among others). To investigate the relevance of such content for users and communities, we present an analysis of reader and editor engagement in terms of pageviews and edits. The results, consistent across 15 diverse language editions, show that these articles are more engaging for readers, and especially for editors. The highest proportion of edits on cultural context content is generated by anonymous users, and also administrators engage proportionally more than plain registered editors. In fact, looking at the first week of activity of every editor in the community, administrators already engage proportionally more than other editors in content representing their cultural context. These findings indicate the relevance of this kind of content both for fulfilling readers' informational needs and stimulating the dynamics of the editing community.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".