Bibliographic record
Abstract
Web search is a common activity in a mobile context. However, the nature of performing tasks in a mobile environment means there is the risk interruption. While the effects of interruptions on mobile search have been studied in recent years, the nature of such interruptions occurring in real-world mobile settings have not. Using a diary study approach, we collected data from 20 participants on the everyday interruptions they faced conducting mobile web searches over a 10-day period. We used inductive open coding to categorise the nature of the interruptions assigning each interruption to a category/sub-category combination. We then used a deductive coding approach to classify each interruption as being either internally or externally-induced; and mobile or non mobile-specific. We found a broad range of interruptions, which we have organised into an extensive taxonomy. Further, a substantial proportion of the interruptions are externally-induced and more than half are unique to mobile contexts. The empirical evidence of the nature of mobile search interruptions in our findings provide insight into the complex environment of mobile search, information upon which to base future mobile search studies (e.g., surveys, controlled laboratory studies), and motivation for the study of search interface designs that can help mitigate the effects of such interruptions.
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 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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".