Results from a survey of the South African GISc community show who they are and what they do
Bibliographic record
Abstract
In the wake of the rapidly increasing global geospatial industry, a shortage of registered GISc professionals, as well as professional GISc registration challenges, have been reported in South Africa. The suitability of registration categories and academic requirements for the type of work performed by GISc professionals has also been questioned. This article presents results of a survey by the Geo-information Society of South Africa (GISSA) to gain a better understanding of who the members of the South African GISc community are and what they do at work. Such understanding is important for the implementation of the new Geomatics Profession Act 19 of 2013, the development of the South African Geo-spatial Information Management Strategy and the establishment of the South African Spatial Data Infrastructure (SASDI). An online questionnaire was distributed and responses analysed. Amongst others, results show that roughly a quarter of all respondents switched to GISc related work later in their career. While individuals tend to focus their work on a few of industries, application areas or disciplines, the GISc community as a whole is active in a wide range of industries, application areas and disciplines. Qualifications that do not meet academic requirements for registration are a significant barrier to registration. Most members of the GISc community fulfil roles of data analysis and interpretation, together with data acquisition, data management, and/or visualization/mapping. The research raises questions whether the differentiation between the type of work performed by different registration categories is clear enough; whether an additional registration category is required for professionals from other disciplines who use GIS as a tool; and why many people who focus on remote sensing are not registered as GISc professionals with PLATO. Survey results contribute to the understanding of the supply and demand for GISc knowledge and skills in South Africa. Additional research is required to better understand the demand and to identify prominent gaps in GISc skills and knowledge.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".