The influence of social contacts on leisure travel: A snowball sample of personal networks
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".