Expanding Toolkits for Heritage Perpetuation
Bibliographic record
Abstract
From 2010 to 2013, the White Mountain Apache Tribe and the University of Arizona, with funding from the National Science Foundation, hosted the Western Apache Ethnography and Geographic Information Science Research Experience for Undergraduates. Designed to foster practical skills and scholarly capacities for future resource managers and anthropologists, this field school introduced Apache and non-native undergraduate students to ethnographic field research and GIS tools. Building upon the extensive arrays of geographical, cultural, and historical data that are available for Western Apache territory, field school students engaged in community-based participatory research with Western Apache elders and tribal natural and heritage resource personnel to contribute to the Western Apache tribes' efforts to document their cultural histories, traditional ecological knowledge, local understanding of geography, and issues of historic and contemporary resource management. This essay reviews the program and traces how student alumni have incorporated skills and perspectives gained into their subsequent academic and professional work.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".