Evaluating performance over time: Is improving better than being consistently good?
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
In many decision contexts, people evaluate others based on intertemporal performance records and commonly face a choice between two distinct profiles: performance that is consistently high versus performance that improves over time to that high level. We proposed that these two profiles could be appealing for different reasons, and thus evaluators' preferences will differ across decision contexts. In three studies, participants were presented with candidates (e.g., students, employees) displaying the two profiles, and evaluated each candidate in terms of performance, future expectations, and deservingness. The consistent candidate was rated higher on performance, but lower on future expectations, than the improved candidate. Consequently, in achievement-based decisions (e.g., selecting a student for a scholarship), the consistent candidate was viewed as most deserving, whereas in potential-based decisions (e.g., selecting an employee for promotion), the improved candidate was preferred. These effects were mediated by the relative weight that evaluators placed on performance and future expectations.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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