Applying the multi-view framework to assess national spatial data infrastructures with particular focus on the Dutch SDI
Bibliographic record
Abstract
Results Canadian, Colombian and Spanish SDIs in each assessment view performs relatively better than others Nepalese SDI in each assessment view performs relatively worse than others.Differences between approaches results, but not very high SDI readiness view vs. Clearinghouse suitability view Correlation coefficient = 0,69 SDI readiness view vs. State of Play view Correlation coefficient = 0,54 SDI clearinghouse suitability view vs. State of Play view Correlation coefficient = 0,44 Different views are not highly correlated which means that they measure different aspects of SDI (are not redundant) Results Conclusions Multi-view assessment framework shows broader picture of each country SDI This allows for more objective and less biased NSDI assessment Multi-view framework application will be continued using more than 4 assessment views, using experts that evaluate a selective number SDIs, and sampling more countries.Challenge: Use multi-view framework to facilitate the monitoring of GIDEON -under discussion.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".