Bibliographic record
Abstract
An issue of USURJ takes form through case-by-case selection of exceptional student work, without consideration for theme; this is in keeping with the nature of a multidisciplinary journal. It is a pleasant inadvertency that, although issue 2.1 is penned by a diverse academic community, it yields notable thematic cohesion.In fact, cohesion is itself variously focused upon by issue 2.1, through its authors' research into issues of personal and societal well being. One researcher supports the incorporation of culturally meaningful healing processes into mainstream medical care. Another highlights the importance of the patientdoctor relationship and long-term, consistent health care, by proposing a tool for improving continuityof-care during medical professionals' residency training. We learn about the potential of combining GIS mapping, soil test data, and socioeconomic survey results to boost West African smallholder farmers' profitability and community well-being, by enabling efficient enrichment of nutrient-deficient soil. Finally, the politically and socially unifying power of music with a cause is demonstrated, through punk band D.O.A.'s part British Columbia's labour movement in the 1980s.We hope you will be as delighted as we are by the work of our featured visual artist, Stephanie Mah, as she explores breaks and connections between human and nature, and delicately twists them into powerful moments for us to experience.The knowledge and ideas shared in issue 2.1 certainly demonstrate the relevance and power of publicly engaged undergraduate research. Similarly, as the collaborative product of student editors, researchers, faculty, and reviewers, USURJ represents our desire to improve, to disseminate knowledge, and to contribute meaningfully to a larger community of research.Thanks to the diligence and initiative of our contributors, USURJ has established itself as part of the University of Saskatchewan academic community. Consequently, both volunteers and research submissions are increasing in number and, for the first time, we are now able to release two issues in one academic year.We have many contributors to thank for building up USURJ, and we do so with genuine appreciation. We hope that our hardworking authors, reviewers, and editors are as proud as we are of their promotion of valuable undergrad research! Thanks are particularly due to Liv Marken (staff advisor) and Kathleen James-Cavan (faculty advisor), who have been with USURJ since its inception, providing stability and expertise to the journal and its volunteers.Happy reading!Nicole Haldoupis, Graduate Editor-in-Chief& Caitilin Terfloth, Undergraduate Editor-in-Chief
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.042 |
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".