GIS Capacity Building in the Pacific Island Countries: Facing the Realities of Technology, Resources, Geography and Cultural Difference
Bibliographic record
Abstract
Historically, development projects in GIS and related disciplines implemented by Western agencies in developing countries have reflected the importance placed on advances in technology and method by Western GIS literature and industry. Such projects, which often focus on human development activity, attempt to replicate operating environments in the West within organizations in the developing world. Such an approach reflects a modernization model for development and is, it is suggested, potentially inappropriate for the needs of developing countries. Based on preliminary work conducted as part of an overall study of GIS development in the Pacific Island countries, this article examines some of the problems surrounding GIS education and training efforts in the region. While recognizing the importance of GIS to developing countries, the article draws a comparison between approaches that reflect the interests of those supplying and those receiving such assistance. Problems cited in the implementation of effective education and training programs include an emphasis on technical issues, an unclear knowledge and skill base for GIS, short-term and disparate educational and training opportunities, and a small user community in the Pacific Island countries. Different types of educators and trainers are profiled, as well as different types of learning opportunities. It is suggested that learning projects that focus primarily, and at all stages, on the needs of recipients and are implemented over longer time frames stand a greater chance of success and will have enduring effect. The importance of regionally based development and educational organizations that provide longer-term support to local GIS organizations is highlighted. An argument is made for including human resource development components in all GIS projects executed by external agencies as well as for the systematic support of user groups and other mechanisms that both initiate and nourish GIS awareness and understanding. The emphasis on diverse and longer-term learning opportunities is tied to a suggested "richer" approach to GIS development - an approach that addresses the wider needs of individuals working in GIS and takes into account different learning styles found outside the developed world. Lastly, it is suggested that more research is required that draws together GIS and development theory in order to more carefully assess the nature and impacts of GIS implementation in the developing world.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".