From data collected traditionally to administrative data : dilemmas and perspectives
Bibliographic record
Abstract
Authors of the chapter depicted existing theoretical concepts and current scientific achievements associated with public data and its use for conducting evaluation research. The current situation and recent improvements in general availability of government data in the OECD countries were presented based on the estimates of the 2014 OECD Open Government Data Survey. The analytical, legal and institutional challenges of working on administrative data (such as time between subsequent rounds or complex data structures) were thoroughly discussed. International experiences in administrative data use were described referring to the good practices from Sweden (where systemic solutions are the most developed), the United Kingdom and Australia – where certain information from registers was made public owing to the high level of coordination between several government agencies responsible for administrative data collection. The authors also presented the current state and future prospects for using administrative data in monitoring and evaluation processes - for example, infrastructure solutions such as the Polish Integrated Analytical Platform project. Authors referred, among others, to the model of a journey towards a mature data management model, and presented steps needed to develop a technology solution enabling the easier data management (granted by data brokers) - giving the example of Statistics Canada. Finally, authors assessed the scope of potential improvements in the area of the Polish public policy and enhanced availability of administrative data, by offering suggestions of changes that would facilitate scaling and sharing of data for the purposes of their analysis and evaluation.
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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 | 0.305 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.013 | 0.082 |
| Scholarly communication | 0.057 | 0.069 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".