Alaska Native Resilience: Voices from World War II. By Holly Miowak Guise
Bibliographic record
Abstract
How can storytelling and oral history work in tandem with archival sources? How does national memory shift when Indigenous voices are included? Who is best suited to doing this work? In her study of World War II in Alaska, historian Holly Miowak Guise (Iñupiaq) consults tribal and community archives while also conducting over ninety oral histories with Alaska Native elders, non-Native elders, and war veterans from 2008–2022. Her inclusion of Indigenous voices highlights the diversity of Alaska Native experiences in wartime Alaska, a state that today features 228 federally recognized tribes, which are “linguistically diverse and geographically distant” (p. 6). Particularly admirable is Guise’s community-based and ethical commitment to building reciprocal relationships with her interviewees, whom she recruits through a snowball method. She describes this long-term mutual exchange in a section entitled “Alaska Native Oral History Methods” (pp. 16–19) noting her positionality as a “young Iñupiaq woman” (p. 18) committed to Indigenous feminist practices of forging solidarities. She does this through recurrent visits, gift exchange, and through visual documentary practices such as annual “research photobooks” (p. 18) that chronicle her interviewees and her Alaska travels. She also creates space for elder input and feedback during her transcription process through in person visits, snail mail, and phone calls.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".