Self-control, goal interference, and the binge-watching experience: An event reconstruction study
Bibliographic record
Abstract
High-speed internet connections and online streaming services gave rise to the possibility to binge-watch multiple television shows in one sitting. Binge-watching can be characterized as a problematic behavior but also as an enjoyable way to engage with television shows. This study investigates whether self-control explains the valence of binge-watching experiences as measured using the event reconstruction method. The study tests whether lower levels of trait self-control predict higher levels of negative affect and lower levels of positive affect during binge-watching. Additionally, the study tests whether these relationships are mediated by situational aspects of self-control (plans, goal interference, or automaticity). Regression analyses show that participants with higher trait self-control report lower levels of tiredness, boredom, guilt, and sadness when binge-watching compared to less self-controlled participants. These associations are partly explained by binge-watching interfering less with higher order goals for highly self-controlled participants. Lower levels of trait self-control are also associated with a stronger increase in happiness on initiating binge-watching and increased feelings of guilt after binge-watching. Overall, the study suggests that binge-watching is particularly pleasant when it does not interfere with other goals, which is more likely the case for individuals with high trait self-control.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".