Bibliographic record
Abstract
Brock Health Magazine features articles that cover all aspects of health to ensure the coverage of all interests, such as mental, spiritual, physical, and emotional health. The magazine is completely run and written by students from all faculties, for all of the Brock Community. Publishing Issue 19 was a challenge as we all share the struggles of the COVID-19 pandemic. Regardless, Brock Health Magazine has not compromised on the quality and has brought to you topics that fellow students would like to share and write passionately about. Our featured article for this year is the implications of systemic racism on the Canadian health care system, taking a deeper look into the very real consequences of our current path. This year's issue has 13 articles such as the impacts of COVID-19 on mental health, fitness from home, utilizing failure as a key to success and many more. Thank you to BUSU for sponsoring this year's issue. Many thanks to the executive team, writers, editors, and graphic design team for their part in creating this fine journal during extraordinary circumstances, and thank you, the reader for your support over the years. We truly hope you enjoy reading and we look forward to bringing you the next issue in the near future! Happy Reading!
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.711 | 0.639 |
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".