Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 it