Licensing Open Data: The case of the Kenyan and City of Cape Town open data initiatives
Bibliographic record
Abstract
Open data practice is gaining momentum in the public sector and civil society as an important mechanism for sharing information, aiding transparency, and promoting socio-economic development. Within this context, licensing is a key legal mechanism that enables re-use without sanction. However, there is evidence of a “licensing deficit” and this raises questions regarding best practice and sustainability in emerging African open data initiatives, particularly in the context of intermediaries being encouraged to exploit shared data for economic and social benefit. This article asks two main questions: (1) What is the current state of open licensing in two African open data initiatives; and (2) to what degree is it appropriate to focus on licensing as a key indicator of openness? Utilising a case study approach, the research explored licensing dynamics in the Kenya Open Data and the City of Cape Town Open Data initiatives, examining the contexts in which these initiatives were established and their resulting licensing frameworks. The cases reveal evidence of strategic engagement with content licensing, driven largely by the need for legal protection, adherence to international best practice and attraction of the user base required in order to ensure sustainability. The application of licensing systems in both contexts does, however, suggest an emerging system in which data providers are “learning by doing” and evolving their licensing practice as portals and their associated policy frameworks mature. The paper discusses the value of open data licensing as an indicator of organisational change and concomitant importance of taking into consideration the institutional dynamics when evaluating the organisational licensing frameworks of city, national and other governments.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".