A comparative analysis of stakeholder roles in the spatial data infrastructures of South Africa, Namibia and Ghana
Bibliographic record
Abstract
Spatial data infrastructures (SDIs) at various levels (global, regional national, local and corporate) are being developed by and in countries around the world. We assess here the SDI developments in three African countries, Ghana, Namibia and South Africa, using the SDI models developed by the Commission on Geoinformation Infrastructures and Standards of the International Cartographic Association (ICA), focusing on the stakeholders and their roles: the Policy Maker, Producer, Provider, Broker, Value-Added Reseller (VAR) and End User. SDI development in all three countries has involved a variety of stakeholders and has taken a long time, waxing and waning depending on the availability of funding and the commitment of the stakeholders, particularly the Policy Makers. This research on the similarities and differences of the SDI stakeholders in Ghana, Namibia and South Africa improves the understanding of SDI development and we hope that the results can help other countries with their own SDI developments. Based on our work, we make recommendations for refining the ICA’s stakeholder typology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".