And the winner is . . . ? Forecasting the outcome of others’ competitive efforts.
Bibliographic record
Abstract
People frequently forecast the outcomes of competitive events. Some forecasts are about oneself (e.g., forecasting how one will perform in an athletic competition, school or job application, or professional contest), while many other forecasts are about others (e.g., predicting the outcome of another individual's athletic competition, school or job application, or professional contest). In this research, we examine people's forecasts about others' competitive outcomes, illuminate a systematic bias in these forecasts, and document the source of this bias as well as its downstream consequences. Eight experiments with a total of 3,219 participants in a variety of competitive contexts demonstrate that when observers forecast the outcome that another individual will experience, observers systematically overestimate the probability that this individual will win. This misprediction stems from a previously undocumented lay belief-the belief that other people generally achieve their intentions-that skews observers' hypothesis testing. We find that this lay belief biases observers' forecasts even in contexts in which the other person's intent is unlikely to generate the person's intended outcome, and even when observers are directly incentivized to formulate an accurate forecast. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".