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Record W2538341755 · doi:10.1111/apps.12087

Toward an Integration of Goal Setting Theory and the Automaticity Model

2016· article· en· W2538341755 on OpenAlexaff
Gary P. Latham, Jelena Brcic, Alana Steinhauer

Bibliographic record

VenueApplied Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsRogers Communications (Canada)University of the Fraser ValleyUniversity of Toronto
Fundersnot available
KeywordsSubconsciousAutomaticityPsychologyGoal settingTask (project management)Set (abstract data type)Social psychologyVolition (linguistics)Cognitive psychologyGoal orientationCognitionComputer scienceManagement

Abstract

fetched live from OpenAlex

Two laboratory experiments were conducted to assess the extent to which goal setting theory explains the effects of goals that are primed in the subconscious on task performance. The first experiment examined the effect on performance of three primes that connote the difficulty levels of a goal in the subconscious. Participants ( n = 91) were randomly assigned to one of three conditions where they were primed with either a photograph of a person lifting 20 pounds (easy goal), 200 pounds (moderately difficult goal), or 400 pounds (difficult goal). Following a filler task, participants were asked to “press as hard as you can” on a digital weight scale. Participants who were primed with the difficult goal exerted more effort than those who were primed with the moderate or easy goal. The second experiment examined whether choice of goal difficulty level can be primed. Participants ( n = 133) were randomly assigned to one of two conditions. Those primed with a difficult goal consciously chose to set a more difficult goal on a brainstorming task than those who were primed with an easier goal. Similarly, their performance was significantly higher. Conscientiousness moderated the subconscious goal–performance relationship while the self‐set conscious goal partially mediated the subconscious goal–performance relationship.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.611

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)0.0010.000

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 head0.072
GPT teacher head0.424
Teacher spread0.352 · 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 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

Citations43
Published2016
Admission routes1
Has abstractyes

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