Bibliographic record
Abstract
My journey as a student partner began in the summer of 2020, amidst overwhelming COVID-19 hysteria and an opportunity to explore a new kind of online learning. I was doing research with a professor of mine and invited to a workshop about Pedagogical Partners with Alison Cook-Sather. When I learned how impactful my work could be as a student partner, I was hooked. For many years, I had felt as though students were not listened to, heard, or respected. I wanted students to be able to play an active role in their own learning. My freshman year, I became an undergraduate teaching assistant (UTA). The experience truly changed my college experience - I mended my troubled relationship with school and learning and came to love it. Now, in my final year of college, I’ve been a UTA every semester since my freshman year. I’ve done research on the number of UTAs across the country and on campus at Syracuse University (SU). At SU, UTAs were few and far between, just about 10% of classes had one. The most common answer as to why UTAs weren’t used was that there was no money to pay them, so I created my own class that gave students 3 credits to be a UTA and partnered up with two close faculty mentors of mine to teach it. We reflected, dished out advice, and made important bonds with faculty and each other. After one very successful semester, we combined with the Partnership for Inclusive Education (PIE) Program through the Center for Teaching and Learning Excellence to merge the UTA program with a student consultant program. This semester, I am enjoying the best of both worlds as a UTA as well as a lead student consultant. Over the course of my college career, I have learned what a true partnership means, experienced the two-way learning it provides, and worked to make courses more inclusive, understanding, and student-centered. I’ve fought hard to put students in decision-making roles and mentored countless students into fantastic student partners that have changed campus for the better.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".