Does the use of smartphones affect discretionary trips? An analysis of smartphone use data from Halifax, Nova Scotia
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
This paper explores the impact of smartphone apps on discretionary travel by utilizing a survey of smartphone users in the Canadian city of Halifax, Nova Scotia. Both subjective and objective measures of discretionary trips are analyzed. A number of attributes such as smartphone use for different purposes, individuals’ perceptions and attitudes towards smartphone use and travel, and built environment measures are examined along with socio-demographic characteristics. Overall, results suggest that greater use of smartphone apps increases the number of discretionary trips. Perceptions and attitudes toward app use and travel also affect the number of discretionary trips. The results suggest that those who agreed that smartphone use has improved their daily life are more likely to make fewer social, shopping, and entertainment-related trips. The impact of socio-demographic and built environment attributes is low compared to the other variables considered in the analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it