The influence of social contacts on leisure travel: A snowball sample of personal networks
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
In a joint project the Institute for Transport Planning and Systems (IVT) of ETH Zurich and the Institute for Land and Sea Transport (ILS) of TU Berlin collect information on personal networks to investigate the influence of these networks on leisure travel.The project will model the influence and implement the results in advanced agent based travel simulations.The survey methodology follows the egocentric network approach, by asking respondents for information on a specific part of their social network: Leisure contacts.Unlike most studies using this method to survey isolated network components this project combines it with an ascending sampling strategy, called snowball approach, to survey connected egocentric network components to obtain information on the topology of the (total) network.As the survey is still in the field the paper aims to present the survey methodology and -instrument and give an overview on the data collected so far.The main focus of this descriptive summary lies on the size and structure of the personal networks, their spatial distribution and the question how people stay in contact with respect to the geographical distance between them.By giving a brief introduction to similar studies in transport planning, their results, and some basic concepts from social network analysis the potentials of the present project will be highlighted.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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