Never the twain shall meet? An intellectual history of participatory mapping
Bibliographic record
Abstract
Participatory mapping is a broad and diverse field of research and practice that involves non-expert mapmakers in gathering, documenting, and sharing data on spatially bounded issues, creating maps that reflect place-based knowledge. The field encompasses a range of approaches, from countermapping to Public Participation GIS (PPGIS), which differ in their influences, methods, applications, and goals. This diversity has contributed to a lack of consensus on what participatory mapping is, how it is used, and whether a universal methodology exists. Through a scoping literature review of academic papers with 'participatory mapping' in the title or abstract, we have constructed an intellectual history of the field, examining how it has been used, when, where, and for what purposes over time. We find that shifting understandings of participation, the influence of geospatial technologies, and the expansion of participatory mapping beyond geography have been central to its evolution. Our analysis identifies two distinct approaches to participatory mapping, differentiated by their aims of engagement, methodological orientation, emphasis on process or method, common areas of application, publication sources, and geographic contexts. This intellectual history enhances our understanding of participatory mapping research, tracing key trends, changes, and the future trajectory of the field.
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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 | 0.052 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.018 | 0.081 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".