Are Cell Phone Samples Needed for Studies of Walking Activity?
Bibliographic record
Abstract
The growth in cell phone–only households represents a challenge for the collection of survey data. Cell phone–only households have distinct sociodemographic characteristics, which may result in different travel behavior. To explore those differences, as well as to investigate the impact of including a cell phone component in active transportation research, a representative sample of New Jersey households was surveyed with a random digit dial survey that included 1,200 completed interviews (800 based on a statewide landline sample, 400 from a landline over sample of Jersey City) and 311 statewide cell phone interviews, of which 80 were cell phone–only respondents. The survey explored walking behavior and perceived characteristics of the pedestrian environment. Sociodemographic characteristics, the frequency of walking, and home location characteristics were compared with chisquare tests of significance between sample pairs as well as multivariate analysis (ordered probit). Cell phone–only respondents were typically younger and poorer, with a greater proportion of renters, carless households, and minorities. It was found that cell phone–only household members walked more frequently, but this finding was because of their distinct sociodemographic characteristics, not their cell phone use per se. The implication for any analysis of rates or trends in walking (and probably other travel behavior) is that cell phone–only households must be included through a cell phone sample supplementing a landline sample. However, in the absence of a cell phone supplement, multivariate analysis of the correlates of walking may not be overly biased if sociodemographics relevant to cell phone–only respondents are collected and included in the analysis.
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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 | 0.046 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".