Bibliographic record
Abstract
I lande som Sverige, Tyskland og Canada har statsledere undskyldt for statens rolle i bl.a. folkemord og tvangsfjernelse af oprindelige folks børn. I Danmark er der ikke præcedens for statslige undskyldninger, men i 2019 gav den nyvalgte statsminister Mette Frederiksen (S) en officiel undskyldning til tidligere anbragte på børnehjem og lod forstå, at hun var villig til at undskylde i andre sager om statslig misrøgt af socialt udsatte. Fordi genren er ny, og fordi det ofte har været et argument mod at undskylde, at det var der ikke tradition for i Danmark, diskuterer og sammenligner dette kapitel de fortilfælde, som er blevet påberåbt i den danske debat om officielle undskyldninger og munder ud i en analyse af Mette Frederiksens undskyldning. Dette kapitel handler altså om en ny dansk talegenres udvikling.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".