(Re)purposing cadasters: When ecclesiastical archives advocate for Indigenous land rights
Bibliographic record
Abstract
Abstract This paper reflects on the potential and limits of repatriating state‐sanctioned historical materials and repurposing them as “counter‐archives” for Indigenous communities. This proposal aligns itself with the epistemic shift in archival studies which promotes a processual approach to archiving (archive‐as‐subject) rather than an extractive one (archive‐as‐source). Instead of taking colonial archives at face value or dismissing them entirely for their erasures, scholars and artists are finding new ways to approach, produce, and share them. This research expands the scope of counter‐cartography and historical geography by identifying different data sources that can be mapped. Mandated by a Mohawk Land Defender, I have compiled ecclesiastical archives, cadasters, and land registries from the Seminary of St. Sulpice into a “counter‐archive,” then turned them into a geospatial database for use in GIS. Historically these ecclesiastical records were used by the Seminary to claim Indigenous territories, erase Indigenous presence, and attract settlers to the Seigneurie du Lac‐des‐Deux‐Montagnes, an area spanning 540 km 2 west of Montreal. The repurposed counter‐archives can be used as tools for critical public discourse around Indigenous land rights. Given genuine Federal will for reconciliation, this methodology mapping land dispossession from archival cadasters and land registries could expand to other locations across Canada.
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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 | 0.034 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".