Bibliographic record
Abstract
It is more important than ever for institutions to create the conditions that foster student success. Toward this end, many institutions seek to better understand their incoming first-year students. The Beginning College Survey of Student Engagement (BCSSE) annually collects data about students ’ high school experiences and their expectations for the first college year from tens of thousands of firsttime college students prior to their enrollment at four-year institutions in the U.S. and Canada. The most powerful and effective use of BCSSE data is when it can be combined with data from its companion survey, the National Survey of Student Engagement (NSSE). Institutions participating in both surveys receive the BCSSE-NSSE Combined Report that provides an in-depth cross-sectional and longitudinal analysis of their first-year students ’ experiences. There are many possible uses of BCSSE data. They can be used to enhance the first-year student experience by informing the design of precollege orientation programs, student service initiatives, and other programmatic efforts. BCSSE results, especially when linked with NSSE, can be used to shape initiatives that align the first-year experience with recognized effective educational practices. BCSSE-NSSE results can be used in many ways, including:
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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.047 |
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".