Bibliographic record
Abstract
Community mapping is both the recovery and discovery of the connections and common ground that all communities share. This emerging cartographic practice is a vital part of a worldwide movement for participatory learning, community empowerment and sustainable planning. Maps visually represent worldviews and knowledge and therefore have unique spatial power. Community mapping assumes that ordinary people and communities can make maps to express the stories about their lives and home places. Community mapping, as a learning and planning process, facilitates such story telling and community maps represent the stories. This paper begins with an exploration of the power of maps and the theoretical challenge posed by indigenous and community mapping to the discipline of Western cartography. Indigenous maps illustrate the power of maps for cultural, historical and geographic expression and connectedness. They also inspire contemporary community mapping. Profiles of community mapping initiatives in Canada and a case study of Common Ground Victoria are presented with community mapping practitioner observations on mapping methodology and technology. The paper ends with the position that, as the need for community and ecological recovery and connectedness grows, so will the relevance of the unique and powerful spatial learning and planning tool - community mapping.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".