Travel Survey 2020, 2nd Quarter, Person File
Bibliographic record
Abstract
The Travel Survey is conducted four times a year, once every quarter. The main purpose of the survey is to register Norwegian citizens travel habits in Norway and abroad. In addition to data on Norwegian's travel habits, data is also collected about travels, border travels, use of tobacco and alcohol, wood burning, influenza vaccination and attitudes toward immigration. This is the Person File for the 2nd Quarter of the Travel Survey 2020. For every quarter in 2020 a sample of 2 000 persons in the ages 16 to 79 were drawn. In the fourth quarter an additional sample of 1 000 persons were also drawn, and only asked about tobacco and alcohol. There is some variation in the response rate between the quarters, with 56 percent as the lowest in the fourth quarter, and almost 65 percent as the highest in the second quarter. If one look at all the quarters as one, and exclude the additional sample in the fourth quarter, the response rate totals to 59 percent. The most important reasons for non-reply in the Travel Survey 2020 is that we were unable to get in touch with the respondents and that some of them do not wish to participate. It is specially younger persons within the age group 25-44 ears, and people with lower or no education, that is the hardest group to get responses from for the survey. The attached report examines if the non-reply has resulted in deviations from the original sample with regards to the markers age, gender, education, country background and region. We do find some deviation with regards to distribution of age. People in the age between 25-44 years is some less represented and people in the age 45-66 is more represented in the net sample compared to other age groups. The biggest deviation occurs when studying educational level and country background. People with high school or higher education is over represented in the net sample compared to people with lower education. The same applies to people with Norwegian country background compared to people with other country background.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.065 |
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".