Striving for Perfection or Excellence? Implications for Academic Self-Talk and Goal Progress
Bibliographic record
Abstract
Perfectionism is categorized by self-criticism and unrealistic standards, which harm academic achievement, while excellencism is characterized by ambitious and healthy goals, striving, and is related to higher academic achievement. This difference may occur due to how these dispositions influence self-talk styles. Positive self-talk is associated with a higher GPA compared to negative self-talk, which may explain links between excellencism/perfectionism and academic goal progress, but this hypothesis has not been explicitly tested. This study investigated how perfectionism and excellencism influenced students’ self-talk and its impact on goal progress. Carleton students (N = 196) completed measures of excellencism/perfectionism and reported on their daily goal-directed self-talk and academic goal progress for one week. We predict that higher excellencism will be associated with greater positive self-talk (H1a), which will predict greater goal progress (H2a). Similarly, we also predict that perfectionism will be associated with greater negative self-talk (H1b), which will in turn predict less goal progress (H2b). Results will help clarify mechanisms by which dispositions regarding excellencism and perfectionism impact academic goals.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".