Bibliographic record
Abstract
Welcome to Issue 7 of Sciential!As first-time Editors-in-Chief, we are excited to present this issue to you and we hope you enjoy it.We are, as always, committed to providing undergraduate students with the opportunity to publish their work.In doing so, Sciential gives students a platform to present the topics that they are passionate about and feel strongly about.It is more important than ever to foster student lead clubs and organizations while working in an online environment.Though the world is still experiencing isolation, these collaborative initiatives promote a sense of connection among students that we hope is evident throughout this issue.This issue explores a diverse array of topics: the misdiagnosis of endometriosis public health crisis; the sparse reporting of postpartum depression in Canadian news sources and its contribution to stigmatization; determining associations between the colour and heavy element abundance of global clusters; the importance of implementing science communication in science programs and science communication pedagogy; an interview with Dr. Ayesha Khan about her perspective on the benefits of including equity, diversity, and inclusion principles in academic course content.This year, the Sciential team welcomed many new members, which added new perspectives into the publishing process.We would like to thank our Senior Editors, Dalen Koncz and Lavanya Sinha, for their dedication and incredible workethic.Moreover, we want to recognize the diligence and strong commitment of the Sciential Editors.We are also grateful for the contributions of our communications coordinator, Cynthia Chung, in organizing our team's correspondence.As always, we appreciate the incredible work of our Creative Director, Angelina Lam, and the rest of Sciential's Creative Board.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.539 | 0.447 |
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; the direct Gemma label and the distilled Codex classifier 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".