Creating Knowledge for Value Creation in Open Government Data Ecosystems
Bibliographic record
Abstract
Open Government Data (OGD) has grown quickly in the last decade. However, the simple availability of OGD does not mean these data are used well in society. Social actors, both organizations and individuals, must to work collaboratively to create an Open Data Ecosystem (ODE) to manage and deliver OGD. OGD creates value only when the data are analyzed and reused to generate new knowledge. The creation of useful and applicable knowledge is not a simple and permanent thing, as it requires special attention from governments to make the data available and ODE actors to ensure the effective generation of knowledge. Limited research has studied the creation of knowledge in OGD ecosystems and more investigation is required into knowledge work within ODE. This research-in-progress aims to explore and answer the question of how is knowledge constructed in OGD ecosystems.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".