Bibliographic record
Abstract
NISHGA is a deeply personal and autobiographical book that attempts to address the complications of contemporary Indigenous existence. As a Nisga’a writer, I often find myself in a position where I am asked to explain my relationship to Nisga’a language, Nisga’a community, and Nisga’a cultural knowledge. However, as an intergenerational survivor of residential school—both my grandparents attended the same residential school in Chilliwack, British Columbia—my relationship to Indigenous identity is complicated to say the least. NISHGA explores those complications and is invested in understanding how the colonial violence originating at the Coqualeetza Indian Residential School impacted my grandparent’s generation, my father’s generation, and ultimately my own generation. The project is rooted in a desire to illuminate the realities of intergenerational survivors of residential school, but sheds light on Indigenous experiences that may not seem to be immediately (or inherently) Indigenous. Drawing on autobiography, a series of interconnected documents (including pieces of memoir, transcriptions of talks, and photography), NISHGA is a book about confronting difficult truths. NISHGA is also about how both Indigenous and non-Indigenous peoples engage with a history of colonial violence that is quite often rendered invisible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.278 | 0.086 |
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".