Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Todays the needs of graduates who have hard skills or soft skills are increasing, Hardskill is obtained from academic processes such as learning during college, while soft skills are obtained from extracurricular or non-curricular activities that can form distinct and clear student characters. To encourage STMIK AKAKOM students to have good soft skills, a Student Achievement Assessment Guideline was issued as a guideline for the student and student sections in calculating the number of extracurricular and non-curricular activities of each student called the Credit Performance Unit (SKP). Each S1 student must collect 110 SKP, D3 students only need 100 SKP during the lecture period that is evenly obtained in each semester so students must start to have activities from the first semester. Every semester the SKP value must be validated by the student affairs department. Ease of validation and time efficiency is absolutely necessary, therefore a computer-based SKP management system was developed. In the system there is a database for storing SKP data, facilities for students to report their SKP, and facilities for student affairs managers
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.022 |
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 it