Bibliographic record
Abstract
Associated Student Government members must complete at least ten credits at SFCC (with Grade Point average of at least 2.5) while in office must have taken and passed (with a G.P.A of 2.5 or better) at least ten credits at SFCC in the quarter before assuming office. A.S.G. must maintain a quarterly GPA of at least 2.5 while in office. How many credits are you currently taking? Are you an International Student? How many credits did you take last quarter? Are you a Running Start Student? Please state your SFCC cumulative GPA. Last quarter GPA: Please list any prior leadership experience you have: Applicants for Senator positions must have taken or be currently enrolled in at least two classes within the Senator district being applied for. If you are applying for this position, please list these classes: Class 1: Class 2: District: _______________________ If you're unsure about what "districts " are; please come talk to the Academic VP in the A.S.G. office. If applying for a programming position, please rank each programming position in terms of your interest in that specific position. 1st being highest (first choice), and 4th being lowest (last choice). Comedy/Concert Programmer ___ _ Special Events Programmer ____ Lecture/Awareness Programmer ___ _ Outdoor/Outreach Programmer ____
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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.771 | 0.616 |
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".