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Record W2735492531 · doi:10.5465/ambpp.2016.50

Construal Level and Temporal Distance: Revisiting this Relationship Using Subjective Time Format

2016· article· en· W2735492531 on OpenAlexaff
Jing Hu, Sam J. Maglio

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstrual level theoryEstimationPsychologySelf construalTask (project management)Social psychologyCognitive psychologyEvent (particle physics)Computer scienceInterdependenceSociologyEconomics

Abstract

fetched live from OpenAlex

Construal level remains inherently linked to time estimation, a judgment task that can be done both objectively (“How many days from now will it happen?”) and subjectively (“How far from now does that event feel?”), yet the construal literature has not yet examined both constructs simultaneously. This paper reports two studies designed to show the effects of abstract and concrete mindsets on both subjective and objective time estimation and to examine the mechanism underlying the conversion from one to the other. Study 1 revealed a crossover effect such that individuals in high construal reported longer objective time estimation (relative to individuals in low construal level) but shorter subjective time estimation. Study 2 provided evidence that a differential standard of time measurement accounted for this phenomenon. In practical terms, this paper identifies a means by which to allow people think big while still act soon.

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)0.006
metaresearch head score (Gemma)0.035
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.116
GPT teacher head0.385
Teacher spread0.269 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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