Bibliographic record
Abstract
Though not Basque himself, Casey Kennington grew up on a dairy farm in rural Ontario, Oregon, U.S.A., where he first heard the word "Basque" and and met many Basques, including a high school teacher who helped him foster a love for computers. During his university studies, he spent extended amounts of time in Japan, France, and Germany, learning the languages of each country. Since 2016, Casey has been a faculty member of the Department of Computer Science at Boise State University, focusing his research on dialogue systems and natural language processing. His love of languages led him to learn about the Basque language, and, despite being a busy professor, take two semesters of Basque in 2021-2022 at Boise State University. While attending a conference in the Basque Country in 2023, he more deeply understood how unique the Basque people are and why their language is worth learning and preserving. Education Ph.D. - Linguistics - Bielefeld University, Germany, 2016 MS - Cognitive Science - Nancy, France, 2011 MS - Computational Linguistics - Saarbrücken, Germany, 2011 BS - Computer Science - Brigham Young University, 2007 ORCID: https://orcid.org/0000-0001-6654-8966
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.015 |
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".