Pre-entry Attributes and Academic Persistence at the Master’s Level in Science and Technology in Burkina Faso: The Mediating Role of University Experience
Bibliographic record
Abstract
The purpose of this research is to analyze the effect of the university experience (scholarship, repetition) on the relationship between pre-entry attributes (father’s occupation, gender, place of birth, age at first enrollment, field of study in high school, graduate point average [GPA], university enrollment delay in the university, university reform) and academic persistence in master’s degree in science, technology, engineering, and mathematics (STEM) at a university in Burkina Faso. Cox regression and modern mediation analyses are used on longitudinal data from 14 cohorts of freshmen (n = 13,891). Findings revealed indirect-only mediation (father’s occupation [other], field of study in high school, age at first enrollment), complementary mediation (GPA), competitive mediation (university enrollment delay, university reform), and an absence of mediation (direct-only) for gender. There is no mediating effect for the place of birth and the father’s salaried profession. Scholarship programs as well as appropriate reforms and policies aiming to reduce repetition are required to improve academic persistence in master’s degree in STEM.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".