Internet-Based Spatial Decision Support Using Open Source Tools
Bibliographic record
Abstract
In the last half decade, there has been growing interest in the concept of collaborative geographic information systems (GIS) in support of decision making, especially in the context of various domains of planning. This interest has spawned an already substantial literature in what is now becoming popularly known as public participation GIS (PPGIS) or community GIS. A central and general objective of PPGIS is to encourage the use of GIS technology by broadly based and geographically dispersed nonexpert users. In the context of planning decision support, this involves creating software with map-based functionality that is responsive to the needs of user groups that have limited experience with computers and only a rudimentary knowledge of even simple spatial analysis concepts. This functionality should be designed to enable these individuals to communicate and interact with higher level users and agencies on an equal footing so that all participants can be both better informed of each others perspectives and more involved in decision-making processes that involve land and resource use planning and management. This chapter considers the general issue of PPGIS in the context of use of the Internet and the World Wide Web as a means of achieving broad participation and collaboration in decision making among dispersed participants with a diversity of backgrounds and competencies in using spatial concepts and analyses. The chapter also considers the role that open source software tools can play in crafting accessible and highly customizable solutions using an example for assessing the quality of primary-level education in Peru.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".