Authentic pride promotes training progress : A multilevel approach
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".