Bibliographic record
Abstract
I denne artikkelen utvikler og presenterer vi et rammeverk, en digitaliseringskanvas, for å artikulere, beskrive og analysere hvordan en virksomhet kan bli påvirket av teknologisk utvikling og digitalisering. Formålet er å gi ledere et verktøy for systematisk utforsking av muligheter og kunne se potensielle trusler som følger av digitalisering før de oppstår. Bedre forståelse for dette vil kunne føre til mer informerte beslutninger om hvilke valg egen virksomhet skal foreta seg, og bedre innsikt i om de valgene er levedyktige. Vi presenterer tre eksempler på anvendelse av rammeverket, to på bransjenivå (forsikring samt regnskap og revisjon) og en på forretningsenhet (Jotun Hull Performance System). Eksemplene demonstrerer at man gjennom et slikt verktøy kan være forberedt også på disruptive innovasjoner.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".