THE TYPOLOGYCAL APPROACH TO ACADEMIC PERFECTIONISM IN ADOLESCENTS
Bibliographic record
Abstract
Psychological approaches to determining the types of perfectionism in adults and adolescents are considered in the article; the criteria underlying such typologies are analyzed.The psychological features of perfectionism in adolescence are described.The purpose of the research is to investigate the typology of academic perfectionism in adolescents, in the measure of destructive influence, level of academic success and emotional problems.In the study the following psychodiagnostic techniques were used: Questionnaire by A. A. Rean (diagnostic the motivational tendencies for success or avoiding failure), Children-Adolescents Perfectionism Scale by P. Hewitt and G. Flett (indicators of general perfectionism), Depression Beck Scale (adolescents version) and Multidimensional Anxiety Scale by E. E. Malkina.Also, for each student, the average level of academic achievement was determined (the average mark for all school subjects on a 12-point rating scale).The analysis of correlation between the level of general perfectionism in adolescents and the level of academic success, the degree of anxiety and depression manifestations, dominance of motivational tendencies for success or avoidance of failure is done.Using cluster analysis, the typology of perfectionism is developed in adolescence, which takes into account the following criteria: "avoiding failure -hope for success"; "high level of success -low level of success"; "no emotional
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".