To Our Readers: Learning the Importance of Utilizing Skills of Scientific Inquiry beyond the Classroom
Bibliographic record
Abstract
In addition to being a period of growth for WURJHNS, this past year was also a major learning experience for everyone involved with the Journal. Our undergraduate reviewers (with the guidance of Faculty members) learned how to apply review methodologies to critically appraise articles, articulate constructive feedback to authors, and see a paper through to publication. Additionally, our academic affairs team also experienced a completely unanticipated level of success in the form of overwhelming interest from the general student body looking to get involved with research and scholarly publishing. One of our most successful initiatives was the running of 3 seminars presented by students and a Faculty member, Dr. Jamie Melling, on how to get involved with research professionally. Each of the three seminars was completely full and registered by students at all levels of their undergraduate career, indicating the strong interest students at Western have towards applying their knowledge in the form of research.
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 | 0.020 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".