Do open government data (OGD) portals show signs of knowledge management (KM) practices?: an empirical investigation
Bibliographic record
Abstract
Open Government Data (OGD) is a build-up of the data accumulated in the government organisations pertaining to the structural and functional dimensions and it is imperative for OGD to be high-value for facilitating value creation and innovation. The present study purports to provide a launchpad to the aforementioned truism by advancing the concept of Open Government Data Capital (OGDC) resting on the principles of Knowledge Management (KM) given that the high-value OGD can result only with the engagement of the concerned administrative agencies in knowledge sharing for being made accessible for wider use via dedicated web portals. To drive home the arguments, an empirical investigation is conducted with four top-notch countries, viz., Canada, Australia, New Zealand and the United States, in terms of the quantitative evaluation of their OGD portals’ quality and inferences are drawn as to how OGDC may be furthered with the provision and maintenance of high-value datasets. Thus, it is shown that the Australian OGD portal is qualitatively robust and leads in terms of OGDC which may be beefed up with more integration of the KM practices in terms of the inter-governmental agencies’ coordination and the other countries are lagging behind in terms of the quality parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".