Familiar Wikidata: The Case for Building a Data Source We Can Trust
Bibliographic record
Abstract
Wikipedia is far from perfect. The same can be said of its sister project, Wikidata. And yet, excluding the World Wide Web itself, Wikipedia and Wikidata together represent the world’s largest structured humanities data source. This methods paper offers an introduction to the value of Wikidata for humanities research and makes the case for humanities researchers’ intervention in its development. It concludes with a short case study to illustrate how Wikidata can support humanities research projects. The case study project, Linked Familiarity, uses Wikidata data about the people quoted in the first ten editions of Bartlett’s Familiar Quotations to look for patterns in the people Bartlett’s Familiar editorial team thought readers find quotable from 1855 and 1910. These patterns will, we hope, clarify a corner of the zeitgeist: Bartlett’s Familiar Quotations readers voted with their purchases—the book’s popularity suggests the quotes the volume’s editorial team compiled really did meet a public desire, or even need. The Linked Familiarity’s team is using Wikidata data to find out about the people worth quoting in this 55-year stretch, to examine the characteristics that unite them, and to uncover the outliers.
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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 | 0.117 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.024 | 0.068 |
| Scholarly communication | 0.045 | 0.128 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".