Every Mapping Project Needs a Fire Keeper: Lessons from the Kanehsatà:ke Land Defense Mapping project.
Bibliographic record
Abstract
This paper explores the critical yet often overlooked aspect of maintenance in decolonial and Indigenous mapping projects. Indigenous communities across Canada have developed alliances with university researchers to develop mapping projects that communicate their relationships to land to outsiders. However, without ongoing maintenance and care, maps can deteriorate or be repurposed in ways that can be harmful to Indigenous communities. I introduce the “fire keeper” as a person or group of people tasked with maintenance, care, and responsibility for the life cycle of maps incorporating Indigenous data. Using the Kanehsatà:ke Land Defense mapping project developed with a Kanehsatakeró:non Land Defender as a case study, I describe how the role of the fire keeper facilitated the adaptation and evolution of the map in response to the Land Defender’s changing objectives. Maintaining the Kanehsatà:ke Land Defense mapping project became an exploration of options rather than a rush to deliver an output. Based on a series of four semi-structured interviews that I conducted with (1) a campaigner, (2) a digital media strategist, (3) university students, and (4) a Québécois history enthusiast, the Land Defender was able to make strategic decisions about how the Kanehsatà:ke Land Defense mapping project should be deployed and which objectives and audiences, if any, would best support the reclamation of Kanehsatakeró:non lands while also protecting their geospatial and archival intellectual property. The paper concludes by encouraging mapmakers to dedicate more time, energy, and resources to map maintenance than they currently do.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".