Bibliographic record
Abstract
Publishing a journal is the work of many people. The people listed below have been integral to the creation and production of The Arbutus Review.Teresa Dawson, Director of the Learning and Teaching Centre, who created the JournalInba Kehoe, Scholarly Communications and Copyright Officer of the Library, who shepherds the online portion of the JournalLaurie Waye, Managing Editor of the Journal and Manager of the Writing CentreMichael Lukas, Guest editor of the JournalShu-min Huang, Journal Designer and Coordinator of the Writing CentreAll submissions are reviewed blind by at least two readers. These readers are graduate students, researchers, staff, and alumni from the University of Victoria. We thank them for their valuable contributions to The Arbutus Review.Akina Umemoto Helen KennedyAnirban Kar Ilijc AlbaneseBehn Skovgaard Andersen Jennifer SmithBethany Coulthard Jonathan SchmidBrendan Boyd Judy WalshBrian Coleman Julia Serena ReadyBrian Vatne Leslie BraggCarrie Hill Linnea Gay PerryChristina Suzanne Marion SelfridgeClarise Lim Scott KouriConstance Sobsey Sheri GitelsonEmma Hughes Stephanie FieldHeike LettariWe also thank the instructors who supported their students’ submissions. Their weaving together of research into the undergraduate experience has enriched their students’ education.Dr. Alexandra D’Arcy, Department of LinguisticsDr. Charlotte Schallié, Department of Germanic and Slavic StudiesDr. Daniella Constantinescu, Department of Mechanical EngineeringDr. Jillianne Code, Faculty of EducationDr. Kevin Walby, Department of SociologyDr. Laura Cowen, Department of Mathematics and StatisticsDr. Martin Adams, Department of Pacific and Asian StudiesDr. Rustom Bhiladvala, Department of Mechanical EngineeringDr. Valerie Irvine, Faculty of Education
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.036 |
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; both teacher heads agree on what is shown here.
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".