Public participation geographic information systems (PPGIS): challenges of implementation in Churchill, Manitoba
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public participation geographic information systems (PPGIS) increasingly are utilized in geographic research, yet researchers rarely are provided with guidance on how to implement PPGIS in an appropriate and effective manner. This article reports on the process of research that explores responses to current and future local tourism development offered by a sample of residents using a modified PPGIS approach called ‘community action geographic information system’ (CAGIS). The conceptual development of CAGIS is reported and the challenges encountered during its implementation in Churchill, Manitoba during 2005–2007 are reviewed. It is suggested that researchers wishing to conduct similar research should undertake thorough preliminary fieldwork to assess the likelihood of finding agreement on a common problem; acquiring adequate resources; establishing collective responsibility for the project's outcome; attaining stakeholder support; developing trust and meaningful relationships; and incorporating indigenous knowledge appropriately. Feedback of results to community members also should be an integral part of the research process. A number of feedback mechanisms are reported, including an interactive weblog, which helped facilitate communication between heterogeneous groups in Churchill. Although ambitions for a truly participatory GIS approach to this project have been set aside, it is held that PPGIS can yield positive outcomes for communities and academia. Sharing this research experience will be useful to others who venture into PPGIS research, especially in northern communities .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it