Balancing the Books: How Student Employment During the Semester Affects Academic Achievement
Bibliographic record
Abstract
This study addresses the perceived impact of university students’ outside employment on academic performance and emotional well-being. A number of studies (Blaga 2012, Wenz and Yu 2012, Torres et al. 2010) have found that university students who work to support their studies achieve lower grades and experience more stress. Students at a Canadian university were given anonymous surveys which yielded information on whether they worked, what kind of work they did, and hours a week devoted to that work. They were also asked qualitative questions about their ability to balance their work and their studies, and their beliefs about the impact of outside work on their academic achievement and well-being. The findings suggest that the majority of students, given the choice, would not choose to do outside work. The majority of students believe that such work negatively affects their ability to excel at their academic work. The majority also mentioned stress as an important associated factor. However, a minority of students believe there are advantages to working during the semester, including learning how to manage time and developing a strong work ethic. The study will be of interest to psychologists, counsellors, academics and others who work with students within a post-secondary setting.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".