Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it