Bibliographic record
Abstract
First-Year Seminar (FYS) is an introductory class offered to first-year students to help them acclimate to the college environment, develop effective strategies for studying, and learn techniques that will allow them to swiftly complete small assignments and sizable research projects. In 2014, approximately 80 percent of universities offered FYS, and students who took the course, on average, were less likely to transfer to another school and more likely to receive higher grades. The class allows students to learn more information about the college, select courses that are related to their majors and/or minors, effectively utilize resources while they are studying, cooperate with other students to complete projects, and appreciate the benefits of taking a particular course. FYS also enriches the experiences of first-year students by helping them find organizations of interest, understand university policies, and pursue hobbies while attending the college. At some colleges, students who have already taken a FYS course volunteer to become mentors who provide assistance to first-year students while they are taking the class. Analysis has shown that a high percentage of new enrollees indicated that mentors had a very positive impact on their overall experiences. Moreover, at many colleges and universities, there were increases in the retention rate.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.196 | 0.057 |
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".