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Time as a Situational Constraint to Role-Identity Performance

2016· book-chapter· en· W2513537278 on OpenAlexaboutno aff
Philip S. Brenner

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Power and Status Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSalience (neuroscience)NormativeSituational ethicsSocial psychologyPsychologySurvey data collectionIdentity (music)Social identity theoryStructural equation modelingConstraint (computer-aided design)Political scienceCognitive psychologyStatisticsSocial groupMathematics

Abstract

fetched live from OpenAlex

Overreporting of normative behavior on surveys has prompted identity theorists to question how well survey respondents report the salience of normative identities. This study examines how a lack of time constrains role performance and adds error to measures of religious identity salience. Time diary data from the 2005 and 2010 Canadian General Social Surveys provide a measure of the key outcome, role performance, avoiding the bias inherent in conventional survey measurement. A series of structural equation models predict role performance using three key variables—identity prominence, salience, and time constraints—and the interaction of salience and constraints. Findings suggest a negative effect of constraints on role performance but no effect on identity salience leading to bias in survey reports of salience of certain types of role performance.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.055
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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