Power, Participation, and Inflexible Institutions: An Examination of the Challenges to Community Empowerment in Participatory GIS Applications
Bibliographic record
Abstract
Participatory GIS (PGIS) applications provide tools that allow underprivileged groups to make a case for recognition, participation, and political access. These community-based applications have therefore become the focal point for claims about public participation and empowerment. However, empowerment is a difficult and complex process necessitating the transformation of bureaucratic organizations into flexible institutions that address the concerns of marginalized groups in society. This process involves shifts in power relations during which PGIS organizations confront deeply embedded structures and vested political interests. Opposition from local leaders, unfamiliar customs and rituals, and lack of infrastructure and skilled GIS personnel impede successful participation and empowerment. Additionally, reliance on external sources of funding and expertise for PGIS projects severely limits their long-term sustainability. To date, PGIS applications have produced case studies about attempts to empower communities, but few studies have focused on how the community-based organizations and the contexts of PGIS applications mediate the community empowerment process. This article explores ways in which the internal and external environments of a PGIS organization influence the community empowerment process. Experiences from PGIS studies in southern Ghana are used to illustrate the constraints that these factors impose on community empowerment.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.047 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".