Bibliographic record
Abstract
Kongelige prædikater, som f.eks. ”Leverandør til den Kongelige Danske Hof”, har fungeret som gyldne kvalitetsstempler i århundreder, og med tiden er de blevet vævet tæt ind i fortællingen om Danmark. For mange virksomheder, er prædikaterne blevet en central del af deres selvopfattelse, men hvad betyder det egentlig, når et produkt bærer kongeligt anerkendte symboler? Og hvordan reagerer danske forbrugere på, at det nu skal være slut med kongeligt porcelæn og royalt marcipanbrød? Gennem interview med danske forbrugere og direktører, kaster vi lys på, hvordan det kongelige stempel påvirker forbruget og virksomheders omdømme. Kongehusets hygiejnefaktorer kan nemlig ikke længere adskille hofleverandøren fra konkurrenten, og Kongehusets tradition med at føre udvalgte virksomheder frem med guldstøv, er ikke længere politisk acceptabel.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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; both teacher heads agree on what is shown here.
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".