A longitudinal investigation of trait‐goal concordance on goal progress: The mediating role of autonomous goal motivation
Bibliographic record
Abstract
OBJECTIVES: The present study investigated the benefits of matching personality traits with goal type (i.e., agentic or communal) for goal progress. Autonomous motivation was examined as a mediator. METHODS: A multi-wave prospective longitudinal design was employed to track the progress that 935 university students made in their personal goal pursuits over an academic year. Participants set three personal goals at baseline and completed measures of personality and goal motivation. Participants' goals were coded as being either agentic or communal. Goal progress was assessed mid-year (T2) and at the end of the academic year (T3). Goal motivation was reassessed mid-year (T2). RESULTS: Conscientiousness was significantly related to making better progress on agentic, but not communal, goals. Conversely, Extraversion was related to making communal, but not agentic, goal progress. These trait-goal matching effects on progress were partially mediated by goal-specific motivation, suggesting that the selection of goals that matched one's traits resulted in higher autonomous motivation at the start of the academic year. CONCLUSIONS: The selection of trait concordant personal goals is associated with autonomous goal motivation and greater goal progress. This research integrates Self-Determination Theory with trait theories of personality to enhance our understanding of variations in goal success.
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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 | 0.004 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".