Geographies of Indigenous Leaders: Landscapes and Mindscapes in the Pacific Northwest
Bibliographic record
Abstract
This essay features three stories of “place-based” leadership in two Indigenous communities in the Pacific Northwest. Author Michael Marker weaves together stories from Nisga'a Elders in the Nass Valley of British Columbia, Coast Salish Elders in Washington State, and his own experiences as a researcher, teacher educator, and community participant to connect the personal, the political, and the historical themes of Indigenous education. Marker identifies two salient concepts through the developing narrative: first, leaders from an Indigenous consciousness must invigorate traditional spiritual foundations, and, second, they must mobilize knowledge of the land and people—corroded by colonization—toward cultural renewal. Bringing to light the conflicts between local community yearnings and Western institutional goals when engaging in cross-cultural collaborations, this essay puts forth a decolonized approach to educational leadership, one that requires cultural renewal and respect for how a people experience landscape, history, and identity.ErratumPublisher's Note: Due to an editing error, the original published version of “Geographies of Indigenous Leaders: Landscapes and Mindscapes in the Pacific Northwest” by Michael Marker misstated the present status of the Lummi Day School. The earlier version stated on page 230 that “This school is currently a U.S. government institution that serves students from kindergarten through eighth grade.” The sentence has been corrected to read: “This school was a U.S. government institution that served students from kindergarten through eighth grade.”Updated: 2015-09-30
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".