LIS Journals’ Lack of Participation in Wikidata Item Creation
Bibliographic record
Abstract
There are many items in Wikidata representing scholarly articles. However, these items have been created mostly by volunteer Wikidata editors and not systematically by journal publishers or editors, which can lead to gaps and inconsistencies in the datasets. This article presents findings from a survey investigating practices of library and information studies (LIS) journals in Wikidata item creation. Believing that a significant number of LIS journal editors would be aware of Wikidata and some would be creating Wikidata items for their publications, the authors sent a survey asking 138 English-language LIS journal editors if they created Wikidata items for materials published in their journal and follow-up questions. With a response rate of 41 percent, respondents overwhelmingly indicated that they did not create Wikidata items for materials published in their journal and were completely unaware of or only somewhat familiar with Wikidata. Respondents indicated that more familiarity with Wikidata and its benefits for scholarly journals as well as institutional support for the creation of Wikidata items could lead to greater participation; however, a campaign of education about Wikidata, documentation of benefits, and support for creation would be a necessary first step. The article presents and discusses the results of the survey, but the conclusions that can be drawn are minimal; therefore, the authors also discuss the benefits of creating Wikidata items for LIS journals as a first step in this educational campaign for editors and publishers.
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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 | 0.060 | 0.308 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".