Bibliographic record
Abstract
Feb.26 2008. 16 pgs. Bread and butter themed issue; odd stories from around the world; Pilot Provincial Nominee Program helping Ontario attract bright international students; a unicyclist is interviewed; the 'bread and butter' of the Pro-Tem staff and Glendon students; new research suggests coffee drinkers may be less likely to develop certain cancers. Contributers: Veronique Bates, Justin Brangman, Heather Campbell, Nikki D'Souza, Samantha Feder, Kimberly John, Sara Laconte, Vasha Maharaj, Carlynn McAneeley, Mark Nimeroski, Andree Paulin, Chantal R., Philip Tetco. Editor-In-Chief: Ashley Jestin Assistant Editor: Clara Wille News: Jesse Reynolds Health and Wellness: Marisa Baratta Campus Life: Stephanie Rudolph Expressions: Jacinto Wong French: Valentine Bruneau D'artois Entertainment: Jacinto Wong Metropolis: Clara Wille Photographer: Irena Kramer Politics: Gabriel Rompre Cartoonist: Laura Sajdik Arts and Culture: Alex Ross Design and Layout: Jacinto Wong, Cliff Davidson Letter from the editor Lettres au redacteur The 5 Wickedest things in the world to do with butter Bread and butter: quick hits Woman, 82, backs over man helping her with directions Au Canada, les criminels apportent leurs petits avec eux en prison Ontario attracting best and brightest international students GCSU update: change in the air at the GCSU office Glendon's top 5 bread and butter Oui suis-je? Ou vais-je? Que fais-je? Interview with a unicyclist Bread and butter Pro-Tem staff, Glendon students Fidel Castro ou la menace fantome Kim Jong-il/going to eat walruses Mugabe ou l'art fin de la phychologie Sweet tears and bitter dreams Wheeler's spin: when hans decides to take it solo Coffee: a cup a day keeps diseases away Reconfort-moi Healthy happenings Recettes de residence Expressions Dear Captain Innuendo Horoscopes Mitch Hedberg Jokes
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.725 | 0.660 |
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".