Perfectionism and time perspectives: An inquiry into the association between perfectionism subtypes and categorical time perspectives
Bibliographic record
Abstract
The present study investigated the previously unexplored association between the perfectionistic personality dimensions, as defined by the 2 × 2 model of perfectionism, and individual time perspectives. The study tested the relationship between pure self-oriented, pure socially prescribed, mixed and non-perfectionist subtypes and individual time perspectives, using a sample of 129 undergraduate students with a mean age of 19.84 ( SD = 4.60). Participants completed a one-time evaluation using the short versions of the Hewitt and Flett Multidimensional Perfectionism Scales and of the Zimbardo Time Perspectives Inventory. Following moderated hierarchical multiple regression analysis, the main result was discovering evidence of a close association between present fatalism and perfectionistic subtypes. Furthermore, pure self-oriented perfectionism consistently predicted more adaptive outcomes in terms of time perspectives as compared to non-perfectionism in four out of the five time perspectives, supporting the emerging empirical distinction between adaptive and maladaptive perfectionism. The study’s overall results indicate that pure self-oriented perfectionism may hold more adaptive merit than non-perfectionism in the case of time perspectives and that present fatalism may hold a close association with the perfectionistic variable.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".