Blackfeet: Campus Voices, Future Echoes
Bibliographic record
Abstract
As a Blackfeet senior student in the University of Montana/Missoula, I am research assistant to Mizuki Miyashita who is a linguistic professor who prioritizes the importance of the Blackfeet language revitalization at the university. Our work is surrounded on vowel changes and dialects because most of the language we use in our work is Blackfoot, which is the sister tribe in Canada, to show our work to people like myself that want to learn if not more about the Blackfeet language. My main interest in the abstract is to focus on what other native or non-native students would like to know more about or the reason to keep the Blackfeet language alive for future generations to come. With most native languages in Montana, every tribe prioritizes the importance of their language because it’s what gives us identity, culture and a sense of belonging for Native American heritage. On the university campus, there are a lot of native students and non-native students willing to learn the language like the Blackfeet or Amspaki Pikanii. The language itself is very hard to learn due to the authenticity and pronunciation of the words that are spoken by first language speakers, such as the older generations of the tribe. Yet what has helped with speaking the language are the recordings that had been approved by the Blackfeet elders. This is where my research helps with students that have taken interest in the language and they can help pass on the Blackfeet language to future generations.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".