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Record W4200089008 · doi:10.31234/osf.io/f7epj

Difficulty-as-improvement: The courage to keep going in the face of life’s difficulties

2021· preprint· en· W4200089008 on OpenAlexaboutno aff
Veronica X. Yan, Daphna Oyserman, Gülnaz Kiper, Mohammad Atari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLeadership, Courage, and Heroism Studies
Canadian institutionsnot available
FundersHarvard UniversityJohn Templeton Foundation
KeywordsMindsetImpossibilityFace (sociological concept)KarmaCourageTask (project management)PsychologyPerspective (graphical)Identity (music)RecallChinaSocial psychologySociologyAestheticsCognitive psychologyEpistemologyPolitical scienceHistorySocial scienceLawComputer scienceManagement

Abstract

fetched live from OpenAlex

When a task or goal is hard to think about or do, people can infer that it is a waste of their time (difficulty-as-impossibility) or valuable to them (difficulty-as-importance). Separate from chosen tasks and goals, life can present unchosen difficulties. Building on identity-based motivation theory, people can see these as opportunities for self-betterment (difficulty-as-improvement). People use this language when they recall or communicate about difficulties (autobiographical memories, Study 1; “Common Crawl” corpus, Study 2). Our difficulty mindset measures are culture-general (Australia, Canada, China, India, Iran, New Zealand, Turkey, the U.S., Studies 3-15, N = 3,532). People in WEIRD-er countries slightly agree with difficulty-as-improvement. Religious, spiritual, conservative people, believers in karma and a just world, and people from less-WEIRD countries score higher. People who endorse difficulty-as-importance see themselves as conscientious, virtuous, and leading lives of purpose. So do endorsers of difficulty-as-improvement –who also see themselves as optimists (all scores lower for difficulty-as-impossibility endorsers).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.316
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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Same topicLeadership, Courage, and Heroism StudiesFrench-language works237,207