The Student Movement Volume 108 Issue 1: '23 and me: Welcome to the AU Family!
Bibliographic record
Abstract
HUMANS Babbling at the Crayon Box, Anneliese Tessalee Dorm Sweet Dorm, Savannah Tyler Surviving Freshman Year 101, Colin Cha ARTS & ENTERTAINMENT AU's Reception of "Barbie", Amelia Stefanescu "Hey, How Was Your Summer?", Nailea Soto Sewing as an Art Form: My Experience as a First-Time Formal Dressmaker, Daena Holbrook Shadow & Bone: Reentering the Grishaverse, Madison Vath NEWS Another Generation, Another Convocation, Melissa Moore Canada's Fiery Struggle: The Ongoing Battle Against Wildfires, Brendan Oh Labor Day, the Writers' Strikes, and Fairness, Nathaniel Miller IDEAS Antibiotic Resistance, Sumin Lee Chapel Credits: Fair or Unfair?, Corinna Bevier From Flowers to Fires: Does Climate Change Rhetoric Need to Change?, Bella Hamann Suicide Prevention Month and the Power of Support, Reagan Westerman PULSE All That and Then Summer, Lexie Dunham Food Near AU, Alyssa Caruthers Mirror, Mirror on the Wall, is There a Fairest of Them All?, Anna Rybachek Social Media Fasts, Rodney Bell II LAST WORD You Are a God Who Sees Me, Chris Ngugi
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.580 | 0.337 |
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".