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Record W2159626140 · doi:10.1177/1948550610370214

Being Unpredictable

2010· article· en· W2159626140 on OpenAlexaff
Oscar Ybarra, Matthew C. Keller, Emily Chan, Stephen M. Garcia, Jeffrey Sanchez‐Burks, Kimberly Rios Morrison, Andrew Scott Baron

Bibliographic record

VenueSocial Psychological and Personality Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologySocial psychologyInterpersonal communicationPerceptionContext (archaeology)Interpersonal interactionCompetition (biology)Interpersonal relationshipDevelopmental psychology

Abstract

fetched live from OpenAlex

Psychological research has devoted much attention to how people judge and predict others. However, a full understanding of social perception necessitates incorporating the responses of the targets, who may have little interest in being predicted. The authors argue that whether people want to be predicted depends on the interpersonal context—in particular, competitive or cooperative ones. Study 1 used a unique behavioral measure and showed that competition participants, when asked to draw the flight path of a moth in a separate study, produced significantly more variable and significantly less predictable trajectories than did cooperation participants. Study 2 examined participants' self-assessments and showed that participants expecting a competitive interaction indicated that they were more difficult to predict, less willing to open up, and more willing to mislead. Together, the findings suggest that people are not always open to being predicted and that the form of these tendencies depends on features of the situation.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.003

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 head0.054
GPT teacher head0.417
Teacher spread0.363 · 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 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

Citations14
Published2010
Admission routes1
Has abstractyes

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