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Record W2605142775

Authentic pride promotes training progress : A multilevel approach

2016· article· en· W2605142775 on OpenAlexaffabout
Jenna D. Gilchrist, David E. Conroy, Catherine M. Sabiston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrideFeelingPsychologyAffect (linguistics)Social psychologyExtant taxonPolitical scienceCommunication
DOInot available

Abstract

fetched live from OpenAlex

In performance contexts such as sport and exercise, affect serves as feedback about progress toward important goals. Feelings of authentic pride provide information about success and achievement. Thus, low feelings of pride should signal a lack of progress towards goals and that a change in behaviour is needed. However, the extant literature has primarily focused on average levels of pride rather than fluctuations in pride in regulating behaviour. The purpose of this study was to examine multilevel associations between pride and subsequent training progress. Participants (N = 131, 78% women; Mage = 35.41, SDage = 9.79) were training for a long-distance race and provided weekly self-reports on emotions and training progress. Participants reported their training-specific pride using a state version of the Authentic and Hubristic Pride Scale (AHPS; Tracy & Robins, 2007) and their training progress each week for seven weeks. Multilevel models indicated that training progress was greater following weeks when participants reported lower authentic pride than usual (β = -0.43, p < .001). Findings support that individuals' use their feelings of pride to regulate their behaviour, consistent with the affect-as-information framework and recent findings (Schwarz, 1990; Weidman et al., 2015). Thus, low feelings of pride can be useful in goal-striving insofar as individuals are attuned to their emotions and adjust their behaviours accordingly.Acknowledgments: This research was supported by the Social Sciences and Humanities Research Council of Canada

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.115
GPT teacher head0.356
Teacher spread0.240 · 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.

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

Citations0
Published2016
Admission routes2
Has abstractyes

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