The Student Movement Volume 105 Issue 15: SASA's Saris Shine at Cultural Celebration
Bibliographic record
Abstract
NEWS SASA Hosts "Once Upon a Time in Bollywood", Caralynn Chan Vaccinated: Andrews Students Receive Fist Dose of COVID-19 Vaccine, Taylor Uphus PULSE AAPI Issues on Campus: A Dialogue, Jessica Rim Student Features: The Story Behind the Car, Interviews by Wambui Karanja Summer Plans: STEM Majors, Interviews by Masy Domecillo HUMANS A Quarter Century of Research, Interviewed by Alyssa Henriquez An Interview with Taylor Biek: Next Year's AUSA Social VP, Interviewed by Abigail Lee Makarios Easter Passion Play, Interviewed by Ben Lee ARTS & ENTERTAINMENT April Current Favorite Songs, Hannah Cruse Celebrating National Poetry Month, Alannah Tjhatra Creative Spotlight: Karen Garcia, Interviewed by Megan Napod Signal Boost: Rookie Historian Goo Hae-ryung, Hannah Cruse IDEAS A Birthday Boy's Reflections: What I'm Learning Now, Adoniah Simon Biden's Job Plan: The Latest Example of Government Investment in Our Future, Lyle Goulbourne THE LAST WORD My (Last) Last Word, Daniel Self
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.197 | 0.047 |
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".