ON GETTING BETTER AND WORKING HARD: USING IMPROVEMENT AS A HEURISTIC FOR JUDGING EFFORT
Bibliographic record
Abstract
There is a strong conceptual association between improvement and effort. Therefore, we propose that people tend to use improvement as a heuristic for judging effort in others. Hence, they would perceive greater effort in improved performance records than in non-improved records with superior overall performance. To examine whether people use improvement as a heuristic for effort, we compared judgments of effort investments and trait effort in improved and consistently-strong performance profiles with equivalent recent performance. Across six empirical studies, participants thought that those with improved profiles exerted more effort and were more hardworking than those with consistently-strong profiles, and this resulted in a preference for improved candidates when making decisions (e.g., selecting among candidates for a promotion). Even when we introduced manipulations that highlighted strengths of the consistent profiles, participants still made effort judgements in favour of improvement (Studies 2 and 3). Moreover, participants had a greater tendency to mention effort as a reason for selecting an improved (vs. consistently-strong) candidate for an award (Study 4). Furthermore, two studies (Studies 5 and 6) showed that the use of improvement as a heuristic for effort was restricted to contexts with considerable ambiguity. Finally, we examined the overall effects using meta-analyses (Study 7). Overall, the results provided converging evidence that people use improvement as a heuristic for judging effort, particularly in contexts that are relatively ambiguous, and that these judgments can have implications for important decisions.
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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 | 0.026 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| 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".