Fulfilling the University Promise: Enriching the Art of Mentoring with Counseling Methods and Empirical Evidence.
Bibliographic record
Abstract
Over a quarter of a century ago the classic study by Astin (1975) reported that freshmen most likely to drop out of college were those with poor high school academic records, low aspirations, poor study habits, relatively uneducated parents, and small town origins. More recently, the national emergence of problems associated with immigration, substance abuse, poverty, and the migration of gang violence from urban to rural communities (Valencia, 2002) are impacting Astin’s college drop out framework. Following the publication of the findings by Astin and endeavoring to learn from their own attrition experiences, many colleges and universities incorporated innovative approaches to better meet the diverse and growing needs of their students. One particular campus of the California State University system is a comprehensive metropolitan university located in the center of California’s agricultural heartland. This campus is one of many universities incorporating approaches to better address the needs of their students and serves as the focus of this paper. Throughout the paper the campus will be identified as the University.
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 | 0.103 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".