Academic Buoyancy Measurements of First Year Engineering Students at the University of Saskatchewan
Bibliographic record
Abstract
As part of program evaluation and continuous improvement, the University of Saskatchewan deployed a survey across the common Re-Engineered First-Year (REFY) program in September 2022, and collected just over 300 responses. This survey included four questions developed by Martin and Marsh [3] to measure academic buoyancy, which these authors define as the ability to cope with everyday academic setbacks. In this study, the highest buoyancy question score (slightly positive) referred to dealing with setbacks, and the lowest (slightly negative) referred to the ability to deal with stress. Statistical analyses show a correlation between the responses to the two buoyancy questions related to stress, as well as between the responses to the two buoyancy questions related to setbacks. There is a statistically significant difference in all academic buoyancy scores between male and female respondents (male scores are higher). Buoyancy scores for students participating in high-performance competitive sports also tended to be higher than those in recreational sport activities. Members of clubs also tended to have higher scores than students engaged in other extra-curricular activities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".