Bibliographic record
Abstract
My daily tasks consisted of office hours with students that were held in the Centers on campus (VU 7th Floor), responding to emails from administrators and students, and representing the interest of students in all applicable spaces. The Centers space that I held my office hours in is home to the Ethnic Student Center, Blue Resource Center (Serves our Undocumented, DACA and Mixed Status Students on Campus), LGBTQ+ Western and the Disability Access Center. This past quarter I focused my work on quite a few areas. I had previously worked with our Survivor Advocacy Center on campus during my previous term as the VP for Diversity. During this term, I focused on working with the director to increase events held on campus. I had gotten feedback from students requesting some in-person activities. This can be tricky due to the center not wanting survivors or community members to feel as if they attended an event, they would be publicly labeled as a survivor of sexual abuse or domestic violence. A great solution was found by myself and the director to hold yoga sessions on campus with an instructor that had a background in trauma therapy. The event was well attended and received positive feedback. The Center also made an effort to table on campus more so students would be more aware that services like the Survivor Advocacy Center is available to students.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.749 | 0.358 |
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".