Testing if Healthy Perfectionism Enhances Academic Achievement in Australian Secondary School Students
Bibliographic record
Abstract
Although considerable evidence has confirmed that measures of intelligence and conscientiousness substantially predict academic achievement, other personality variables have attracted only limited research. The purpose of this study was to test the extent to which intelligence and personality variables, including perfectionism, accounted for academic grades. Participants were 180 (65 males) secondary school students in years 11-12. They completed tests for fluid and crystallised abilities (Gf, Gc), Conscientiousness (C), Openness to Experience (O), Neuroticism (N), Need for Cognition (NFC) and the Frost Multidimensional Perfectionism Scale, which was used to define healthy perfectionism (HP) and unhealthy perfectionism (UHP). Gender differences for all measures were negligible and not considered further. One aspect of HP (personal standards) overlapped moderately with NFC but HP and NFC appeared to be different constructs. Hierarchical regression found that Gf, Gc and C together accounted for 27% of variance in academic grade, with HP explaining an additional 6%. Further contribution from NFC was not statistically significant. N correlated with UHP but did not impact grade. Higher concern about parental criticisms correlated (r = -.27) with lower academic grade.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".