Metadata for Everyone: Identifying Metadata Quality Issues Across Cultures
Bibliographic record
Abstract
Metadata is crucial to the dissemination and communication of research. Quality metadata facilitates discovery and access and provides contextual, technical, and administrative information in a standard form. Yet metadata are also sites of tension between sociocultural representations, resource constraints, and standardized systems. Formal and informal interventions may be interpreted as metadata quality issues, political acts to assert identity, or strategic curatorial choices to maximize discoverability and visibility. This presentation documents the work of Public Knowledge Project (PKP) and Crossref on the Metadata for Everyone project to understand how metadata quality, consistency, and completeness impact individuals and communities. Working from a sample of records known to have erroneous, incomplete, or otherwise imperfect metadata, we set out to identify and classify issues stemming from how metadata and communities press up against each other to intentionally reflect (or not) cultural meanings.
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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 | 0.057 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".