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Record W4411531099 · doi:10.1177/27000710251347134

Exploring goal change in everyday life

2025· article· en· W4411531099 on OpenAlexaff
Tyler Thorne, Isabelle Leduc‐Cummings, Anamarie Gennara, Marina Milyavskaya, Chelsea Kisil, Sierra Micucci

Bibliographic record

VenuePersonality Science · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsMcGill UniversityCarleton University
Fundersnot available
KeywordsGoal pursuitGoal settingPsychologyPerceptionEveryday lifeGoal orientationBehavior changeSocial psychologyApplied psychologyPolitical science

Abstract

fetched live from OpenAlex

Research on goal pursuit often assumes goals remain stable. Yet goal pursuit is a dynamic process where goals and perceptions of them can change, of which our understanding is limited. Past research has mostly focused on the role of performance and negative feedback in laboratory or experimental studies. We investigate what predicts goal change and how goals change over time in everyday life. Participants ( N = 420 North American undergraduate students; 75% female) reported on two goals biweekly for three months ( n = 1505 follow-up observations). People changed goals on 5.4% of occasions, although 27.38% of participants changed goals at least once over three months. Other (non-academic) goals were changed more frequently than academic goals. Multilevel models revealed commitment, goal progress, time, and stress predicted goal change. People were less likely to change goals later in the study and when they were highly committed, but more likely to change goals when they made very little or a lot of progress (quadratic effect), and when they experienced greater stress. Whether they changed or kept their goals, their perceptions of goal difficulty, commitment, and self-efficacy changed over time. We discuss implications of such changes for theories and research on personal goal pursuit.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.304
Teacher spread0.174 · 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 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
Published2025
Admission routes1
Has abstractyes

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