Bibliographic record
Abstract
The University of Victoria, in many ways, is a special place. It is one of the few universities in Canada where Indigenous issues are taught, discussed, and debated with the attention and care they deserve—and thanks to a cadre of excellent faculty and instructors, the debate has been a respectful one. The sizeable Indigenous faculty presence on campus, as well as a variety of programming options has created a healthy space for Indigenous scholarship. Perhaps one of the most important aspects of UVic is the constant acknowledgement that UVic is situated on the lands of the Coast and Straits Salish people. The presence of local Indigenous peoples—students, faculty, staff, and community members—as well as Indigenous peoples from further afield, makes for an enriching intellectual and social environment for those of us who study Indigenous issues here. In this atmosphere, learning extends to places outside of the classroom and provides for dynamic relationships with new people from different places with different perspectives. The University of Victoria has, quite deservedly, also developed a reputation as a world leader in Indigenous Studies, something that I have been reminded of at the many conferences I have attended across the continent. It is well known for producing some groundbreaking scholarship and attracting world-class students.
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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.132 | 0.062 |
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".