Grades, motivation, and resilience: the role cognitive and non-cognitive traits play in the undergraduate research student selection process
Bibliographic record
Abstract
Abstract The academic research experience is extremely rewarding but is also froth with many challenges, setbacks, and frustrations. The publish-or-perish mentality of a research-intensive academic environment demands a certain skill set in its trainees. This selection criterion begins with the undergraduate research selection process, but what is the ideal skill set of an incoming student entering the research experience? The current landscape on this topic is bleak, with little examination of how students are chosen for these positions. We therefore conducted an analysis examining student selection methods, non-cognitive traits, emphasis on grades and medical school future ambitions. Our findings suggest that the top five student traits valued by principal investigators are: motivation, resilience, hard work, inquisitiveness and honesty. Surprisingly, emphasis on grades as a screening tool decreased as age of laboratory and frequency of publication increased. Additionally, we identified an inverse correlation between student interest in medical school and research supervisor interest in selecting the student for an undergraduate research experience. Taken together, our study culminates in a defined set of skills beneficial for an incoming student at the beginning of their research experience. We feel our findings will greatly facilitate the overall undergraduate student selection process in any academic environment.
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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 | 0.012 | 0.058 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".