National Spatial Data Infrastructure in Botswana – An Overview
Bibliographic record
Abstract
The spatial data plays a vital role in any developmental activities whether it is natural resource management or socio-economic development. Most land-related government departments in Botswana have over the years since independence in 1966 developed systems to support their principal areas of operations as regards to spatial data. The adequacy and currency of spatial data in government operations improved leading to a need for integrated systems. This has progressively led to issues of building a National Spatial Data Infrastructure (NSDI) and an initiative modeled around Federal Geographic Data Committee (FGDC) has been established. Several facilitative committees were set and several meetings held in attempt to develop the idea to a realizable level and integrate it into the greater workings of the national economy. This noble idea has stalled for some time now and it is the intention of this paper to report on how the idea was initiated in Botswana and look at the probable causes for its stalling. The paper will then go ahead and suggest what could be done to revitalize the idea by relating it to what is considered the best practices in Spatial Data Infrastructure (SDI) programmes globally.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.013 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".