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Motivation towards extracurricular activities and motivation at school: A test of the generalization effect hypothesis

2016· article· en· W2558097971 on OpenAlexafffund
Anne‐Sophie Denault, Frédéric Guay

Bibliographic record

VenueJournal of Adolescence · 2016
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec-Société et Culture
KeywordsDeci-PsychologyContext (archaeology)MediationTest (biology)DisadvantagedSelf-determination theoryAutonomySocial psychologyDevelopmental psychologyGeneralizationMathematics education

Abstract

fetched live from OpenAlex

Participation in extracurricular activities is a promising avenue for enhancing students' school motivation. Using self-determination theory (Deci & Ryan, 2000), the goal of this study was to test a serial multiple mediator model. In this model, students' perceptions of autonomy support from their extracurricular activity leader predicted their activity-based intrinsic and identified regulations. In turn, these regulations predicted their school-based intrinsic and identified regulations during the same school year. Finally, these regulations predicted their school-based intrinsic and identified regulations one year later. A total of 276 youths (54% girls) from disadvantaged neighborhoods were surveyed over two waves of data collection. The proposed mediation model was supported for both types of regulation. These results highlight the generalization effects of motivation from the extracurricular activity context to the school context.

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)0.009
metaresearch head score (Gemma)0.027
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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations37
Published2016
Admission routes2
Has abstractyes

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