Bibliographic record
Abstract
Editor in Chief | Redactrice en chef: Camille Slaght Assistant English Editor | Redactrice adjointe (anglais): Effie Barbalias Assistant French Editor | Redactrice adjointe (francais): Gabriella Giordan Communications Officer | Agente de communications: Sarah Mullins Photography Editor | Redactrice de photographie: Lauren Clewes Design and Layout | Maquettiste: Sienna Warecki Section Editors | Chroniqueurs Campus Life | Vie etudiante: Reeda Tariq Arts and Entertainment | Arts et divertissement: Bruno Da Costa Metropolis | Metropole: Andrew Thies Issues and Ideas | Actualite et opinions: Sabrina Sukhdeo Health and Wellness | Sante et bien-etre: Emilia Nowicki Expressions: Kaitlin Fenton Online Content | Contenu en ligne: Sam Kacaba In this issue Campus Life: a recap of Frosh Week and a massive Glendon Club Spread! Arts and Entertainment: reviews of Bridget Jones and C’est la vie Metropolis: film nights at Alliance Francaise Toronto Issues and Ideas: international student fees, US presidential debate, and BLM-Toronto Health and Wellness: the importance of wearing a helmet Expressions: horoscopes, hydrophobia, haiku on healthcare, and more!
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.548 | 0.455 |
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".