Emotions and Goal Progress: A Linguistic Analysis of Goal Narratives and its Relation To Goal Progress
Bibliographic record
Abstract
Individuals express a wide range of emotions in their writing beyond positive and negative emotions. In the current study, we set out to examine how positive, negative, and epistemic (curiosity and anxiety) emotions are related to individual goal progress. Instead of relying solely on self-reported emotions, we employed an indirect qualitative analysis using the Linguistic Inquiry and Word Count (LIWC) to examine student goal narratives (N = 201) and self-reported goal progress. Our results suggest that students use various emotions when describing their goals, especially positive emotions and epistemic curiosity. Despite the frequent use, we found no statistical association between emotions and progress toward achieving goals. In order to gain a realistic insight into how multiple emotions are utilized in goal narratives and goal progress, we suggest that future research use a more sophisticated method, such as a longitudinal study or combination of self-report, human coders and LIWC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".