"But it's our story. Read it.": Stories my grandfather told me and writing for continuance
Bibliographic record
Abstract
This is a story about stories. Born out of a ten-year-old request from my grandfather, Ronald Whiteduck, to help him record our family’s and community’s history, this research essay explores the theoretical, ethical, and methodological considerations of writing oral history in Native communities. Punctuated by transcriptions of my grandfather’s stories and my self-reflection, this essay explores Native writing in terms of the responsibilities Native writers have when we write; how writing can keep our people grounded in our home(lands); the potential of writing to decolonize; and, most importantly, writing for the continuance of our nations. When we write, Native writers are responsible to our families, our communities, and the larger Native academic community. Our stories represent a fundamental love and respect for our homeland, and writing them ensures our children can return home regardless of their physical location. Through writing we can achieve decolonization by responding to past and ongoing oppression, while actively moving beyond it. Continuance manifests when we thrive in a space of our own, where our ways of being are combined with tools provided by academia to further our goals. The essay concludes by asking, “Where does it end?”
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".