Technology Supported Behavior Restriction for Mitigating Self-Interruptions in Multi-device Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The interruptions people experience may be initiated from digital devices but also from oneself, an action which is termed “self-interruption.” Prior work mostly focused on understanding work-related self-interruptions and designing tools for mitigating them in work contexts. However, self-interruption to off-tasks (e.g., viewing social networking sites, and playing mobile games) has received little attention in the HCI community thus far. We conducted a formative study about self-interruptions to off-tasks and coping strategies in multi-device working environments. Off-task usage was considered a serious roadblock to productivity, and yet, the habitual usage and negative triggers made it challenging to manage off-task usage. To mitigate these concerns, we developed “PomodoLock,” a self-interruption management tool that allows users voluntarily to set a timer for a fixed period, during which it selectively blocks interruption sources across multiple devices. To understand the effect of restricting access to self-interruptive sources such as applications and websites, we conducted a three-week field trial (n=40) where participants were asked to identify disrupting apps and sites to be blocked, but the multi-device blocking feature was only provided to the experimental group. Our study results showed the perceived coercion and the stress of the experimental group were lower despite its behavioral restriction with multi-device blocking. Qualitative study results from interviews and surveys confirm that multi-device blocking significantly reduced participants’ mental effort for managing self-interruptions, thereby leading to a reduction in the overall stress level. The findings suggest that when the coerciveness of behavioral restriction is appropriately controlled, coercive design can positively assist users in achieving their goals.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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 it