ON GETTING BETTER AND WORKING HARD: USING IMPROVEMENT AS A HEURISTIC FOR JUDGING EFFORT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it