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Record W4415587327 · doi:10.1037/pspa0000468

The failure gap.

2025· article· en· W4415587327 on OpenAlexaff
Lauren Eskreis-Winkler, Kaitlin Woolley, Min-Hee Kim, Eliana Polimeni

Bibliographic record

VenueJournal of Personality and Social Psychology · 2025
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsClosing (real estate)Stigma (botany)Punishment (psychology)Suicide preventionSocial stigmaFear of failurePublic opinion

Abstract

fetched live from OpenAlex

. People underestimate tens of thousands, and in some cases, millions of failures at the individual, national, and international level across 30+ life domains (Study 1). For every three species that go extinct, the public knows of one; for every five weapons undetected by airport security, people think one sneaks by. Why are people unaware of the problems around them? Failure is underreported relative to success (Studies 2-5). The failure gap had policy implications for key decision makers. Closing the failure gap reduced support for harsh punishment (e.g., school suspensions) among educators, lowered support for mass incarceration among voters, led managers to extend paid parental leave to new mothers, and shrank social stigma in the workplace (Studies 6 and 7). Taken together, the failure gap is common, crippling, and encouragingly correctable. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.363
Teacher spread0.330 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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