Country specific characteristics matter
Bibliographic record
Abstract
The analyses in the previous chapters of this book have demonstrated and analysed influences from personal networks on social interactions including travel (see Chapter 3) and a Swiss population-wide network (see Chapter 4). Besides the data collection efforts in Switzerland several similar efforts have been undertaken in different parts of the world with the motivation to investigate influences among socio-demographics, personal network characteristics, mobility biographical aspects, social network geographies and spatial patterns between social contacts. In addition to the data collection efforts in Switzerland (Chapter 2, also see Ohnmacht and Axhausen 2005; Chapter 3, also see Frei and Axhausen 2007; Chapter 4, also see Kowald and Axhausen 2012), there have been also collection efforts in Canada (Hogan et al. 2007; Carrasco et al. 2008), the Netherlands (van den Berg et al. 2009), and Chile (Carrasco and Cid-Aguayo 2012). These studies have highlighted similar observations and the relevance of different aspects of individuals’ personal networks on their activity and travel behaviour.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 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".