Boundary management in action: A diary study of students’ school-home conflict.
Bibliographic record
Abstract
Contemporary technologies enable students to be "connected" with friends, family, student peers, and their study materials 24/7. This study aimed to examine how college students' boundary management enactment (BME; ranging from segmentation to integration) related to school-to-home conflict and home-to-school conflict and, subsequently, to school performance, satisfaction with home life, and home-school balance. Moreover, this study aimed to establish whether these relationships depended on students' boundary management preferences for segmenting school from home, and home from school. A diary study was conducted among 122 students from a major university in the Netherlands. Students completed an online questionnaire and online daily surveys over a period of 5 consecutive days of study. Results supported that students experienced more school-home and home-school conflict when they integrated rather than segmented school and home. Also as predicted, integration related to lower school performance, lower home life satisfaction, and lower balance, and these relationships were mediated by increased conflict between home and school life. Students' preferences did not moderate these relationships. This indicates that segmenting school and home life roles seems to be the advisable strategy for students, irrespective of their preference for segmentation. Students would benefit from increased awareness of the advantages of segmentation and 'how to' training sessions that teach them how to set boundaries between school and home. (PsycINFO Database Record
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".