Perspectives on the Implementation of the CESSDA Metadata Model
Bibliographic record
Abstract
CESSDA Metadata Standards Portfolio, outcome of CESSDA Metadata Management (CMM) Project, includes core metadata model and controlled vocabularies (CV) for relevant metadata fields. The Portfolio was designed to ensure compliance with Data Documentation Initiative (DDI). We present the results of the concluding task of Phase 1 of the CMM project, where we analysed the impact of the proposed Portfolio solution and identified the challenges for its implementation. 13 CEESDA members and 2 additional stakeholders participated in the survey and provided feedback on the proposed model. We evaluated the Mandatory elements of the Portfolio, applied the (adjusted) System Usability Scale (SUS) and investigated the metadata availability. We were interested in how SPs use DDI, especially to what extent they already provide metadata and whether they would be able to adapt the existing solutions to the one proposed in the model. Moreover, we asked SPs about their usage of CV (DDI and ISO). Based on the results we gathered, we also drew some comparisons between Service Providers (SP) at different stages of development. Our presentation concludes with discussing further work in the following Phase 2, where we plan to upgrade our evaluation survey. Moreover, a special focus will be given to exploring the Metadata Model usage possibilities in other organizations apart from CEESDA SP (e.g. domain stakeholders and related organizations).
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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 | 0.090 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".