People focus on optimistic scenarios and disregard pessimistic scenarios while predicting task completion times.
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
Task completion plans normally resemble best-case scenarios and yield overly optimistic predictions of completion times. The authors induced participants to generate more pessimistic scenarios and examined completion predictions. Participants described a pessimistic scenario of task completion either alone or with an optimistic scenario. Pessimistic scenarios did not affect predictions or accuracy and were consistently rated less plausible than optimistic scenarios (Experiments 1-3). Experiment 4 independently manipulated scenario plausibility and optimism. Plausibility moderated the impact of optimistic, but not pessimistic, scenarios. Experiment 5 supported a motivational explanation of the tendency to disregard pessimistic scenarios regardless of their plausibility. People took pessimistic scenarios into account when predicting someone else's completion times. The authors conclude that pessimistic-scenario generation may not be an effective debiasing technique for personal predictions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it