Voices from the survey margins: Investigating unsolicited comments written in children’s activity-travel diaries
Bibliographic record
Abstract
While digitally recording data from hardcopy activity-travel diaries, a team of transportation and health researchers noticed the presence of unsolicited comments on the survey documents. While an immense body of work has been amassed about survey design and analysis, transport scholars have not written about the presence of unsolicited feedback in activity-travel diaries. This paper reports on a thematic analysis of the unsolicited comments written within activity-travel diaries. Two key themes were identified: data quality and respondent affect. Comments about data quality pointed toward possible measurement error due to difficulties incorporating the study into everyday life, or due to human-error. Respondents also offered some additional context for reported data. Affective responses included apologizing for possible data errors and expressions of frustration with the survey. Most respondents who wrote unsolicited comments self-identified as female, of higher education, and employed full-time. The presence of unsolicited comments offered a unique window into the research experiences of the researched, questions and comments raised by participants point toward possibilities in terms of survey design and future research.
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".