Open Data Portal - A Technical Enabler to Drive Innovation in Namibia
Bibliographic record
Abstract
Promising a world of endless opportunities, open data is increasingly becoming a global phenomenon, attracting interests from many governments, such as the United States of America, Germany, United Kingdom, Canada and Kenya to mention a few. Furthermore, institutions such as the United Nations, World Bank and the African Development Bank. Seeking ways to harness value from open data, these countries and institutions have established open data portals to serve as technical enablers, easing access to public data and acting as one-stop shop for a wide variety of data ranging from environment, education, health, transportation, geo-location, budget, weather, consumer products and consumer finance. These portals do not only enable citizens to access a wide variety of datasets but through APIs allow citizens to transform the data into innovative applications or solutions that solve local challenges and societal problems, thus, improve quality of life for citizens. Sequel to the possibilities that surrounds open data portal, it will be helpful to have an open data portal in Namibia. Consequently, to bridge the gap between the citizens, data and information, an open data portal becomes necessary. In this paper we provide practical, yet technical insights on how an open data portal we developed will ease access to information and facilitate the development of innovative solutions that will aid communities in solving societal problems in Namibia.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".