Hvad er Danmarks styrke på digital innovation i den finansielle sektor?
Bibliographic record
Abstract
I denne artikel ser vi nærmere på de strukturelle styrker, der danner grundlaget for Danmarks digitale styrker med udbredelsen af offentlige digitale services og forbrugere, som accelerer i at tage digitale services til sig. Igennem et institutionelt perspektiv fremhæver artiklen nogle af de egenskaber, som karakteriserer digital innovation af bankservices i Danmark. De væsentligste faktorer, som artiklen peger på, er de offentlige investeringer i digital infrastruktur, som skabende troværdige produkter som NEMID-login hos borgere. Vi fremhæver eksempler på dette mellem de etablerede banker på det danske marked og den nye digitale bank Lunar. Artiklens nyskabelse er, at den bidrager med ‘bricolage-perspektivet’ på nye digitale bankservices, hvor eksisterende ressourcer genanvendes på nye måder og i nye konstellationer gennem partnerskaber. Strukturer som danner grundlag for, at digital innovation kan ske i samarbejder, i netværk og partnerskaber som en form for samarbejdsdrevet entreprenørskab. Artiklen bygger videre på et institutionelt innovationsperspektiv, som tilbyder en ny måde at skildre og forstå nøglen til Danmarks digitale innovation på i et konkurrenceprægede miljø.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.126 | 0.057 |
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".