Cross‐cultural invariance of the Academic Expectations Stress Inventory: Adolescent samples from Canada and Singapore
Bibliographic record
Abstract
We provide further evidence for the two-factor structure of the 9-item Academic Expectations Stress Inventory (AESI) using confirmatory factor analysis on a sample of 289 Canadian adolescents and 310 Singaporean adolescents. Examination of measurement invariance tests the assumption that the model underlying a set of scores is directly comparable across groups. This study also examined the cross-cultural validity of the AESI using multigroup confirmatory factor analysis across both the Canadian and Singaporean adolescent samples. The results suggested cross-cultural invariance of form, factor loadings, and factor variances and covariances of the AESI across both samples. Evidence of AESI's convergent and discriminant validity was also reported. Findings from t-tests revealed that Singaporean adolescents reported a significantly higher level of academic stress arising from self expectations, other expectations, and overall academic stress, compared to Canadian adolescents. Also, a larger cross-cultural effect was associated with academic stress arising from other expectations compared with academic stress arising from self expectations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".