Bibliographic record
Abstract
Encouraging contact among students from different economic, social and racial or ethnic backgrounds could help provide the support students deem necessary to succeed at college. Evaluation of a 2011 Community College Survey of Student Engagement (CCSSE) dataset reveals an intriguing relationship between student diversity and students’ feelings of support they need to succeed at college. Analysis of data implies that improving students’ understanding of people of other racial and ethnic backgrounds could help encourage contact among students from different economic, social, and racial or ethnic backgrounds, and this in turn could help university and college students succeed in their studies. Logistic regression analysis shows the strongest predictor of support needed to help students succeed at college is Encouraging contact among students from different economic, social and racial or ethnic backgrounds. Consequently, increasing student diversity, for example, may be an appropriate university or college strategy to help students understand people of other backgrounds. Greater awareness of people from different racial and ethnic backgrounds could promote contact among students with different backgrounds and this could improve the sense of support students think a college could provide them to succeed at school and in the job market
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".