Is There Anything Exceptional about ICT Use While Travelling? A Time Allocation Framework for and Empirical Insights into Multitasking Patterns and Well-Being Implications from the Canadian General Social Survey
Bibliographic record
Abstract
Being involved in multiple tasks at the same time, usually termed multitasking, is a common presence in most people's lives as reported in various time use and travel behaviour studies. In recent years some researchers have explored the role of information and communication technology (ICT) devices in enabling a greater range of activities while travelling, whereas others reported links between ICT and increased time stress. In this paper, the authors investigate the extent to which ICT use during travel differs from uses during other activities, and how such patterns are linked to indicators of well-being. The authors present an extended microeconomic time allocation framework with a tensor-based time constraint which can be used to explain the propensity of certain activities to occur jointly. The authors operationalise our framework using a general linear model and the Canadian General Social Survey 2010 53 (n=13,313), also linking time allocation patterns to indicators of time crunch. The analysis indicates that secondary ICT activities constitute a comparatively low fraction of the time allocated to primary activities (although this is likely subject to underreporting). A number of socioeconomic attributes, such as age, education, and relationship status, are associated with the propensity to engage in ICT-based multitasking. The pattern of individual attributes associated with participation in secondary ICT activities is found to be reasonably consistent across primary activities, including travel. Finally, the authors note that ICT use as a secondary activity can be an indicator of time crunch, but the actual relationship depends on individuals' characteristics and the context of multitasking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".