Navigating through the noise: The effect of color‐coded performance feedback on decision‐making
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
Abstract Many companies use color codes in their internal performance reports to highlight how current performance compares to performance in a previous period. We examine whether the use of color coding affects managers' decision‐making in a resource allocation task. We argue that managers' decision accuracy will be lower if they receive noisier feedback, but that this detrimental effect of noise can be mitigated through color coding. Using two experiments, we find evidence consistent with our theory. Managers who receive reports in which performance increases are color‐coded green and performance decreases are color‐coded red are less affected by noise than managers who receive feedback reports without color coding. Supplemental analyses suggest that color coding induces managers to process feedback in a more holistic manner, which reduces the adverse effect of noise on managers' learning processes. Our findings have several important implications for research and practice.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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