Bibliographic record
Abstract
As issue 2.2 comes together, we reflect on the growth of USURJ over just a few short years. To date, USURJ has received 213 submissions and published 41. It has provided feedback to researchers and worked with them to improve their writing. In addition, 64 student volunteers from the University of Saskatchewan have now gained editorial experience and learned about the inner workings of a peer reviewed, academic publication, and many have worked with the journal for two years or more. This year, we analyzed 17 multidisciplinary undergraduate research journals from across North America, and found that the number of student editors on our USURJ team far surpasses all but three: the Harvard Undergraduate Research Journal, the Lethbridge Undergraduate Research Journal, and the Fordham Undergraduate Research Journal.Thanks to an increasing number of submissions, we are able to release, for the first time, two issues in one academic year. We sincerely value our volunteer editors, who have once again pulled together to make USURJ happen. It is gratifying to be able to not only help make these experiences happen, but also to provide a venue for undergraduates to share their research—and do they ever have some great ideas to share! In this issue, one engaged student approaches and interviews cyclists using Saskatoon’s 23rd Street Bicycle Boulevard to inform his analysis of that infrastructure. One researcher discusses the importance of conservation strategies that look at the impact of an endangered animal’s decline, and the impact of conservation on its ecosystem. Another evaluates whether or not low dose aspirin reduces occurrences of myocardial infarction (a type of coronary heart disease) and provides recommendations for nurses. Another student calls attention to a food crisis affecting northern Canadian communities, evaluates the measures currently in place to help to solve this issue, and provides suggestions and possible amendments to these measures.Once again, University of Saskatchewan undergraduate students have proven the importance of disseminating their research, not only taking it outside of the classroom to the institution, but also to the international stage.Happy reading! Nicole Haldoupis & Caitilin Terfloth Graduate and Undergraduate Editors-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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.042 | 0.053 |
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".