Bibliographic record
Abstract
Transportvaneundersøgelsen (ofte forkortet som TU), har til formål at kortlægge danskernes transportvaner, principielt defineret som al persontransport indenfor landets grænser. Metoden er et stort antal interview med danskere (10-84 år) om transportadfærden ”dagen i går”. Interview gennemføres både pr internet (ca. 20 % af data) og via telefon (80 %). Interviewpersonerne udvælges repræsentativt ved hjælp af CPR- registret og resultaterne opregnes efter geografi, alder og køn. Undersøgelsen er unik, fordi det er den eneste store, danske undersøgelse med kobling af faktisk transportadfærd til en lang række baggrundsvariable. I international sammenhæng er undersøgelsen unik, fordi den kortlægger alle ture med koordinater for hvert rejsemål. Transportvaneundersøgelsen er gennemført med stort set samme indhold siden 1992, dog med en afbrydelse i 2004-5. Ofte omtales data fra årene 1992-2003 (172.000 interview) som det ”gamle datasæt”, og data efter 2005 som det ”nye datasæt” (p.t. ca. 46.000 interview). Siden 1992 er der sket mange forbedringer og andre ændringer i spørgeskemaet, men der er en hovedkerne af spørgsmål, som er med i alle årene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".