Fast-forward to boredom: How switching behavior on digital media makes people more bored.
Bibliographic record
Abstract
= 1,223; six preregistered), we found a bidirectional, causal relationship between boredom and digital switching. When participants were bored, they switched (Study 1), and they believed that switching would help them avoid boredom (Study 2). Switching between videos (Study 3) and within video (Study 4), however, led not to less boredom but more boredom; it also reduced satisfaction, reduced attention, and lowered meaning. Even when participants had the freedom to watch videos of personal choice and interest on YouTube, digital switching still intensified boredom (Study 5). However, when examining digital switching with online articles and with nonuniversity samples, the findings were less conclusive (Study 6), potentially due to factors such as opportunity cost (Study 7). Overall, our findings suggest that attempts to avoid boredom through digital switching may sometimes inadvertently exacerbate it. When watching videos, enjoyment likely comes from immersing oneself in the videos rather than swiping through them. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".