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Record W4318041413 · doi:10.1287/mnsc.2022.4630

Underdogs and One-Hit Wonders: When Is Overcoming Adversity Impressive?

2023· article· en· W4318041413 on OpenAlexaboutno aff
Jerker Denrell, Chengwei Liu, David Maslach

Bibliographic record

VenueManagement Science · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsWonderQuality (philosophy)Competence (human resources)PsychologyComputer scienceMarketingBusinessOperations managementSocial psychologyEconomics

Abstract

fetched live from OpenAlex

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 .

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.231
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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