Mapping Dreams/Dreaming Maps: Bridging Indigenous and Western Geographical Knowledge
Bibliographic record
Abstract
Dreams and dreaming practices are integrated into knowledge-building processes in many indigenous societies, and may therefore represent a source of geographical and cartographic information. This article addresses the incorporation of these practices into collaborative and cross-cultural research methods, especially in the framework of participatory mapping projects conducted with Indigenous communities or organizations. The author argues that dreams and dreaming practices enable the consideration of Indigenous territorial dimensions – such as the sacred and the spiritual, as well as the presence of non-human actors – that are more difficult to grasp through the social sciences or through modern Western mapping methodologies. In addition, this approach invites geographers and cartographers to adopt a culturally decentred concept of the notions of territory, mapping, and participation that goes beyond the positivist premises of Western science and its research methodologies. This text draws from a Mapuche counter-mapping and participatory mapping experience that took place in southern Chile between 2004 and 2006, in which the author took part as a cartographer.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| 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".