Bibliographic record
Abstract
In this article, I argue that in North America, 500 years of cartographic encounters and translations have transformed Indigenous map-making and geospatial technology processes into an amalgam of knowledge systems, science, and technology. To do this I first review the processes of map-making that have been shaped by continual cartographic encounters, exchanges, and translations between American Indians and Euro-Americans. Dichotomies between Indigenous–traditional and Western–scientific are prevalent within the literature, but the boundaries between geographic knowledge systems have always been fuzzy and crossable. This review includes some processes strongly shaped by Indigenous communities, such as ethnocartography and counter-mapping in Alaska and Canada, and GIS processes controlled more by government institutions in the lower 48 US states. Second, I introduce the tenets of a new model – indigital geographic information networks (iGIN) – to describe the heterogeneous processes of encounters, exchanges, and translations merging Indigenous, scientific, and digital technologies into inclusive forms of technoscience. Third, I demonstrate iGIN processes through exploratory research at the university level, using Kiowa-language narratives and network GIS to create a new “third” construct. Finally, following brief concluding remarks, I propose future research directions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".