Bottom dogs on campus: how subjective age and extrinsic self-esteem relate to affect and stress in first semester of university
Bibliographic record
Abstract
The first semester of university can be a difficult transitional period that affects students' psychological well-being, and ultimately, their academic success. Personal resources and vulnerabilities that they bring to the transition may shape their day-to-day experiences. Subjective age (how old one feels) and extrinsic self-esteem (ESE; the extent to which self-worth is based on external sources) were examined as predictors of mean levels of and intraindividual variability in daily affect (positive and negative) and stress in 170 Canadian students tracked for 14 days during their first semester. Consistent with a self-enhancing effect of an older subjective age, regression models found that feeling older predicted higher mean levels of positive affect, and students with higher ESE reported more negative affect unless they felt considerably older than their chronological ages. In addition, an older subjective age and higher ESE predicted higher levels of and more intraindividual variability in daily stress experience. An ESE appears to contribute to negative affect and stress, but an older subjective age might counteract some negative emotion and play a part in positive emotion. As much as an older subjective age is a possible personal resource, its association with stress suggests that it might have some disadvantages for first-year university students, the bottom dogs on campus.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".