Bibliographic record
Abstract
i use “native” and “indigenous” throughout as adjectives, except when quoting. The first is generally common—though not in every field—in Native American and Indigenous studies in the United States. The second, Indigenous, often invokes a more global context and is now preferred in Canada, along with First Nations, Inuit, and Métis.1Close The capitalization of both terms is “important,” as Daniel Heath Justice (Colorado-born citizen of the Cherokee Nation / ᏣᎳᎩᎯ ᎠᏰᎵ) explains, because “it affirms a distinctive political status of peoplehood rather than describing an exploitable commodity.”2Close Upon first mention in the body and substantive notes of Native American and Indigenous authors and artists, I acknowledge textually or parenthetically their public national and cultural affiliations. As a white settler colonist, I seek to witness and respect Indigenous sovereignty and autonomy even as I recognize that statements regarding affiliation are themselves evolving negotiations of personal and communal principles and histories. These affiliations express complex and at times contested genealogies of belonging woven through centuries of colonization, violence, enslavement, and removal, as well as survival, resistance, confederation, migration, intermarriage, adoption, love, and bondedness. Through these affiliations, Native individuals, communities, and tribal nations avow person-and peoplehood.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.039 |
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".