The relationship between entry age and academic achievement for UNBC's graduation cohorts
Bibliographic record
Abstract
Specifically, in response to the changes in student demographics and the location and characteristics of the University of Northern British Columbia, this project examined and evaluated the effect of entry age upon academic achievement for UNBC's graduation cohorts.Participants consisted of2426 graduates (1998 -2003 classes) with entry ages ranging from 17 to 67.Regression analysis was used.Entry age, Gender, Attendance Patterns (Part-time/Full-time), Admission Types were weak predictors of academic achievement for UNBC graduates.Admission GP A was the best predictor across all age ranges, which coincides with current research.The entire cohort was then split into three groups (Youngest =17-20.99,Middle= 21-29.99 and Oldest = 30+).The results were broadly consistent with previous studies on academic achievement measures for the various age groups.When the effect sizes were calculated for these groups, the differences were trivial.Therefore, when attending UNBC, students will have an equal chance of high academic achievement no matter what their entry age, gender, attendance pattern, admit type or admission GP A.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".