A Song Remembered in Place: Tlingit Composer Mary Sheakley (Loo) and Huna Tlingits in Glacier Bay National Park, Alaska
Bibliographic record
Abstract
Songs among the Tlingit of Alaska and Canada are important means for communicating and aligning relationships, knowledges, and emotions among humans, non-human persons, and ancestral lands. As potent expressions of individual and collective identity, heritage, and destiny, songs encapsulate ethnobiological, social, and geographic knowledges in a melodious, interspecific lingua franca. A particular ancestral or communal context, such as a potlatch or u.éex', may call for a spiritual, mournful, or happy song to help effect a transition, for example from mourning to celebration or death to rebirth. Ceremonial songs are typically owned as property and performed by particular Tlingit matrilineal groups, known as clans, or their house groups. However, songs are in the first instance composed by individuals, typically in response to other unique events, such as extraordinary encounters with wildlife, disasters, or other remarkable circumstances. The composers of such songs, both men and women, are respected and honored for their skills. Mary Sheakley (Loo) is one such figure. She composed the song presented here in response to a group of wolves that came to the beach and howled as she and her fellow paddler left their subsistence camp in what is now Glacier Bay National Park and Preserve around the turn of the twentieth century. In 1996, the song was spontaneously remembered by a contemporary elder and younger clan sister to Mary Sheakley, Amy Marvin, who, in turn, taught it to her younger clan daughter during a berry picking trip to Glacier Bay. Later, during that same trip, Amy Marvin deployed the song to cap an impromptu ritual of commemoration for Tlingit relatives that died in a tragic boating accident in the Park in the late twentieth century. The song was thus not only revived but elevated in status to become a “clan song,” which is now considered sacred property (at.óow) and performed during ceremonies, such as the potlatch or u.éex'.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".