Bibliographic record
Abstract
The academy’s ignorance about and resultant bias against Indigenous Americans, their histories, cultures, legal status, and present circumstances have consequences impacting people ranging from American Supreme Court Justices to soccer players. Too often these consequences create disastrous results for First Nations people as well as for the greater society. To address this nescience, university personnel should include Indigenous American studies in their curricula; English professors should teach works by First Nations and American Indian people; and humanities departments should offer Native art and music courses on a permanent basis. Universities should actively recruit, hire, and properly mentor Native students and faculty members. Faculty should engage themselves with student follow-ups and job placements. Professors, editors, and critics should read Native papers and publications from Indigenous perspectives, not Western ones. Students and tribes can also do their part to end academic racism: Indigenous scholars by organizing themselves into associations promoting information exchange and support, and tribal leaders by conscientiously buttressing their members’ progress through financial and political assistance.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".