Bibliographic record
Abstract
Når en samfundsøkonomisk analyse benyttes til at vurdere projekter, sætter man kvantitative mål på de uønskede konsekvenser, som en given projektaktivitet medfører for mennesker, miljø og økonomi. Særligt når man skal vurdere store transportinfrastrukturprojekter, der hyppigt har effekter i årevis efter vedtagelsen, er det vigtigt at kunne træffe “gode” beslutninger. I Danmark er det bl.a. de tre store broprojekter (Storebælts-, Øresunds- og Femernforbindelsen) samt Metrosystemet i København, der har understreget behovet for analyser, der kan støtte beslutningerne. Den overordnede idé er naturligvis at maksimere samfundets velfærdsgevinst. Når et projekt er veldokumenteret, og når såvel gevinster som omkostninger er blevet systematisk vurderet, har man en væsentlig støtte i den videre politiske beslutningsproces. En moderne samfundsøkonomisk analyse oplister alle den nye transportinfrastrukturs mulige effekter og anvender et sæt relative enhedsværdier til at estimere projektets samlede værdi. Herved kan projektets samfundsmæssige værdi udtrykt i kroner og øre gøres op.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".