Considerations for enhancing participation and data accuracy in geospatial research in rural areas: experiences with PGIS in northern Malawi
Bibliographic record
Abstract
Rural environments are experiencing rapid changes that must be explored to understand, enhance, and facilitate positive changes and adapt to detrimental changes. However, the information researchers can obtain about the environment to identify effective management strategies for rural resources is hindered by several factors. Participatory geospatial research presents an approach that integrates local voices to map the facts and values of rural people and represent environmental changes. Here, we draw on more than six years of participatory geospatial research in rural northern Malawi to identify and present various considerations that participatory geospatial researchers and planners should be mindful of when working with rural people to enhance participation in research and improve spatial data accuracy. Based on experiences using various research methods and activities applied in several transdisciplinary collaborative research projects, we posit that rural geospatial researchers should keenly consider i) ethical issues concerning data collection, analysis, and representation, e.g. taboos and sacred spaces, ii) integrating local spatial ecological knowledge of people about the environment, and iii) economic conflicts and gender dynamics that tend to disempower and limit participation in research and affect data quality. Considering these would build rapport between participants and researchers to facilitate active participation and data accuracy.
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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 | 0.066 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".