A Web of Ways: Navigating Student Persistence & Retention A Web of Ways: Navigating the Myriad of Perspectives on Student Persistence and Institutional Retention in Postsecondary Education
Bibliographic record
Abstract
ii Access to education is an important issue for both governments and postsecondary institutions, especially with the shift to a knowledge-based economy in Canada and around the world. Access is only part of the process to developing a knowledge-based workforce; once a student starts his or her postsecondary journey, the focus shifts from issues around access to student persistence. To achieve high levels of student persistence and adequately measure retention rates, retention issues should be approached from both the student and the institution perspective. This paper explores the difference between student persistence and institutional retention and critically analyzes key factors that can impact persistence and retention. It also introduces and analyzes key theoretical perspectives and related retention strategies. The findings of this paper highlight that student retention strategies require a clear understanding of student characteristics as well as strong leadership, and clear communication. Institutions cannot focus on one aspect of student retention as there are
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".