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Primary and Secondary Control in Achievement Settings: A Longitudinal Field Study of Academic Motivation, Emotions, and Performance<sup>1</sup>

2006· article· en· W2012925299 on OpenAlexaff
Nathan C. Hall, Raymond P. Perry, Joelle C. Ruthig, Steven Hladkyj, Judith G. Chipperfield

Bibliographic record

VenueJournal of Applied Social Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyOverconfidence effectRegretPrideAffect (linguistics)Control (management)Academic achievementSocial psychologyLongitudinal fieldLongitudinal studyDevelopmental psychologyManagement

Abstract

fetched live from OpenAlex

The present research represents an application of Rothbaum et al.'s (1982) dual‐process model of perceived control to adaptation in achievement settings. This eight‐month longitudinal field study examined how primary and secondary control influenced end‐of‐year academic motivation (e.g., voluntary course withdrawal), emotions (e.g., stress, regret, pride), and performance (e.g., cumulative grade point average) in 703 first‐year college students. For successful students, primary control related to better performance, higher motivation, and more positive affect. For unsuccessful students, the combination of primary and secondary control resulted in optimal academic adjustment. Unsuccessful students who rely on primary at the expense of secondary control risk serious long‐term deficits in motivation and performance. These findings are discussed with respect to academic overconfidence and control‐enhancing treatments.

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)0.004
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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.020
GPT teacher head0.325
Teacher spread0.306 · 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

Citations57
Published2006
Admission routes1
Has abstractyes

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