Underdogs and One-Hit Wonders: When Is Overcoming Adversity Impressive?
Bibliographic record
Abstract
Success tends to increase and failure tends to decrease the chances of future success. We show that this impact of past outcomes can change how diagnostic success or failure are regarding the competence of an individual or a firm. Succeeding under adverse circumstances is especially impressive when initial failure reduces the chances of success more for low-quality agents than for high-quality agents. Succeeding after initial failure (being a “successful underdog”) can also indicate higher expected quality than succeeding twice if initial success increases the chances of success of all agents to a high level. In different circumstances, the outcome after success can be especially informative about quality, implying that failing after an initial success (a “one-hit wonder”) indicates lower quality than failing twice does. We find effects consistent with our model in data on Canadian professional hockey players and on data from the Music Laboratory experiment: Initial failure combined with eventual success is associated with high quality. The results help to clarify when failure should be attributed to the person in charge or to the situation, when underdogs and individuals who overcome adversity are especially impressive and when a naïve “more is better” heuristic for evaluating performance can be misleading. This paper was accepted by Isabel Fernandez-Mateo, organizations. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4630 .
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".