What Do Indigenous People Have to Tell Us about the Cultural Landscapes They Have Created?
Bibliographic record
Abstract
What do Indigenous peoples have to tell us about the cultural landscapes they have created by their Indigenous knowledge. Human land-use changes impact physical and biological processes at different scales, creating a legacy of cultural footprints on the landscape. Indigenous populations have occupied the Americas for at least the last 30,000 years. They have adapted to an environment that had previously not been occupied by humans. Indigenous populations were seen by European colonizers in the 1400s as inferior people with no written language, primarily stone tools, and a different spiritual system and were therefore seen as having no sophisticated culture compared with the colonists’ European culture. Further European epidemic diseases caused major decreases in Indigenous populations and major destruction of their culture. This cultural destruction has continued into the twenty-first century. Indigenous peoples, however, had their own well-developed cultures that had created a large number of domesticated plants and had developed complex agricultural systems that supported large populations and increasingly sophisticated land-use and culture. This was all cut short by the arrival of European colonizers who could not recognize a culture different from their own. Today, we have started to understand the similarities and differences between the culture of science and that of Indigenous knowledge, which resulted from the development of both in isolation of the other. This book’s objective is to consider how Indigenous populations have lived and managed the American landscape. They have left a footprint that is a combination of their empirical knowledge and their spiritual culture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".