Treasure Bearers
Bibliographic record
Abstract
Heritage institutions often seem bureaucratic and faceless, but law, policy, and practice are traceable to personal values, preferences, and actions. Individuals have been recognized as "agents" in critical theory and archaeology, but aside from celebrity campaigners for high-profile preservation causes and other anecdotal accounts, the people who carry tangible and intangible heritage across generations receive scant attention. Our profiles of cultural practitioners, documenters, and advocates — five bearers of Northern Coast Salish cultural heritage in British Columbia, Canada — identify four personal characteristics that appear to increase leadership effectiveness in heritage stewardship. We suggest that individuals are more likely to achieve stewardship goals when they are (1) personally identified with the heritage; (2) clearly serving collective interests; (3) credible in communications within and across social boundaries; and (4) willing to act on personal commitments, even in risky situations. The lives and works of the five Treasure Bearers profiled here established the baseline terms of reference, data sets, and priorities for the region's first significant collaborations among First Nations, local governments, researchers, and citizens. Their seminal efforts not only paved the way for initial steps toward intercommunity reconciliation, but assured a pivotal role for cultural heritage in an ongoing suite of community-based initiatives to incorporate the most significant and valuable aspects of the past into a regional future all can be proud of.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 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".