Capacitating local governments for the digital earth vision: lessons learnt from the role of municipalities in the South African spatial data infrastructure
Bibliographic record
Abstract
The Digital Earth vision foresees the availability and accessibility of geospatial information to achieve the goals of sustainable development, economic growth and social well-being. In the case of urban areas, up-to-date geospatial information is essential for managing a city towards achieving these goals. The rapid shift from rural to urban areas globally puts pressure on local governments and they often struggle to find and organise the resources required to collect and maintain geospatial information that can help to address urban growth challenges. A spatial data infrastructure (SDI) can facilitate the availability and accessibility of geospatial information towards addressing national objectives, however, the involvement of local governments in an SDI can be a challenge. In this paper, we critique the role of municipalities against the backdrop of the developments of the South African SDI (SASDI) to date. The critique identifies five high-level shortcomings of the SASDI that have led to the limited participation of municipalities. Based on the shortcomings, we provide recommendations for capacitating municipalities through SASDI so that the Digital Earth vision can also be achieved for municipalities. These recommendations are aimed at involving the local sphere of government in a national SDI and are equally applicable to other countries.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".