Bibliographic record
Abstract
The author looks back at her 15-year career practicing heritage management in southern Alberta. Weasel Moccasin highlights key complexities involved with being an Indigenous heritage management practitioner who is charged with managing her own cultural heritage. She critically analyzes different cultural understandings of heritage and how this affects the ways in which cultural material is managed. In Weasel Moccasin’s experience, the Euro-western notion of heritage is sterile and stagnant, while for Niitsitapii, it is dynamic and immersive—very much a living heritage. Euro-western heritage management focuses on “protection” and “exhibition” of heritage, preserving items from the past. In contrast, Indigenous heritage management involves “practice” and “use” of cultural items, preserving the action and knowledge. Weasel Moccasin’s chapter provides insight and advice to heritage management practitioners, especially to those who are Indigenous. Indigenous people and communities are encouraged to enforce heritage management practices that have their cultural values as the foundation, the core. She reminds us that Indigenous peoples are the experts of their own culture and way of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".