Indigenous Online Mapping in Canada - Decolonizing or Recolonizing \nForms of Spatial Expressions?
Bibliographic record
Abstract
Digital cartography technologies have expanded the tool base for Indigenous communities in Canada as a means of representing their lands and the contestation of space. However, critiques of digital technologies question if these tools are a new system of technological colonialism. This study addresses the question of how this technology is being used today and what impact it is having on Indigenous mapping content. Additionally, I ask if the web as cyberspace can be conceptualized as “a third space,” a decolonialized space of communication, recognition, and reconciliation (Soja, 1996; Bhabha, 2004). I theorize that Indigenous ways of knowing and constructions of space align with Lefebvre’s idea of first space, while Western ways of knowing and mapping practices align more closely with his concept of second space. A mix of quantitative and qualitative methods is used to investigate this theory. The former involves content analysis of 26 Canadian Indigenous web mapping sites using a decolonialized methodologies perspective. The qualitative dimension consists of 10 semi-directed interviews with Indigenous and non-Indigenous cartographers, technicians, scholars, and the producers and consumers of online mapping websites. Triangulation of these data sets identified narrative as an emergent theme, including its strong links to Indigenous cultures and processes of decolonialization. I conclude that while online mapping is a potential medium of \ndecolonization, it has not yet fulfilled this possibility. It currently offers a hybrid space for the examination and reclamation of knowledge production but falls short of being a primary location for discussion, communication, and nexus due to a lack of feedback mechanisms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".