Meaningfulness of Effort: Deriving Purpose from Really Trying
Bibliographic record
Abstract
Most people treat effort as something to be minimized, a cost we'd rather not pay. So, what drives some to actively pursue challenging tasks even when immediate benefits are absent? We introduce meaningfulness of effort, a trait reflecting the tendency to derive meaning from effortful pursuits. Across 3 cross-sectional and 2 longitudinal studies (N = 3,323), including student, general adult, and military veteran samples, we demonstrate that Meaningfulness of Effort acts as a stable unidimensional facet of conscientiousness. It is tightly linked to industriousness, with comparable test-retest reliability, and distinct from need for cognition, while also uniquely predictive of wellbeing outcomes. Those higher in meaningfulness of effort pursue and find meaning in more effortful activities. Similarly, they enjoy higher meaning in life, life and job satisfaction, and less psychological distress, burnout, and PTSD symptoms. Importantly, we saw that Meaningfulness of Effort predicted these wellbeing outcomes over longer time lags. Our findings place Meaningfulness of Effort as a useful trait in understanding why some might pursue effort absent of any clear reward; for them, effort feels fulfilling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".