Bibliographic record
Abstract
The mapping of indigenous lands to secure tenure, manage natural resources, and strengthen cultures is a recent phenomenon, having begun in Canada and Alaska in the 1960s and in other regions during the last decade and a half. A variety of methodologies have made their appearance, ranging from highly participatory approaches involving village sketch maps to more technical efforts with geographic information systems (GIS) and remote sensing. In general, indigenous mapping has shown itself to be a powerful tool and it has spread rapidly throughout the world. The distribution of mapping projects is uneven, as opportunities are scarce in many parts of the world. This review covers the genesis and evolution of indigenous mapping, the different methodologies and their objectives, the development of indigenous atlases and guidebooks for mapping indigenous lands, and the often uneasy mix of participatory community approaches with technology. This last topic is at the center of considerable discussion as spatial technologies are becoming more available and are increasingly used in rural areas. The growth of GIS laboratories among tribes in the United States and Canada, who frequently have both financial and technical support, is in sharp contrast to groups in the South—primarily Africa, Asia, and Latin America—where resources are in short supply and permanent GIS facilities are rare.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".