Ideas Challenge 2020/2021 : WikiData Integration with Repository Contents
Bibliographic record
Abstract
Presentation of the work of an Ideas Challenge Team from Open Repositories Conference 2020. The challenge presented is that of WikiData integration with repositories as a way of improving multilingual access to repository contents. Multilingual indexing by search engines and aggregators, and the overall importance of linguistic diversity in scholarly publishing and access is discussed. The results presented include a detailed overview of various metadata standards relevant for representing multilingual WikiData concepts in repositories: HTML5, Dublin Core, DataCite, JATS XML, Schema.org. Two scripts that were written in Python for enriching Repository Metadata with WikiData Concepts and their use on EPrints JSON-LD metadata and a test dataset of publications in information visualization is presented. These scripts use DBPedia Spotlight API to annotate scholarly metadata with DBPedia concepts, and these in turn are used to extract translated labels from WikiData. A resource list of relevant projects is included, as well as some additional examples and notes.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".