Costly Task Takeovers in Human Performance
Why this work is in the frame
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Bibliographic record
Abstract
When working together, employees often need to decide whether to step in and help one another. Do they know when to do so? In a series of experiments, we introduce a novel method that allows us to measure how well a task is performed when one person takes over from another, and the counterfactual they cannot see: performance if the takeover had never happened. Most participants took over for their partners, decreased task performance, and incorrectly believed that they had improved it. This may happen because people do not properly forecast task trajectories over time, and because they step in too early to see how well their partners perform, and fail to exceed that performance. Finally, using these mechanisms, we report two more experiments in which manipulating task visibility and performance trajectories improves takeover efficacy. Takeovers are common in cooperative contexts, but people may not realize when they harm the performances they are meant to improve.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 it