Information production and social value of open data: a conceptual modelling framework for public policy
Bibliographic record
Abstract
The paper aims to investigate the role of conceptual modelling for policy making. In particular, it is argued that the use conceptual schemas may provide an effective understanding of the information asset of Public Administration and its capacity to satisfy the needs of constituencies, thus having a public as well as social value. Consequently, the use of conceptual modelling for policy making and planning is first discussed with regard to the public sector information asset with an example from the Italian Public Administration. Then, the role of conceptual modelling for eliciting social value is considered with regard to open data. Subsequently, a case study is discussed for the open data concerning Hospitals in the United States (US), Canada, and Italy. Finally, a methodology is outlined to support public managers for choosing the data set to be “opened” and exploiting the public sector information asset under a social value perspective.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".