Datalogisk retsforskning: Kan algoritmer finde juridisk centrale domme afsagt af EMD?
Bibliographic record
Abstract
I denne artikel introduceres først til, hvordan forskellige kvantitative metoder kan anvendes til at supplere traditionel retsdogmatisk metode. Herefter anvendes tidligere studier i denne måde at arbejde på som udgangspunkt for at udvikle en metode til at udpege domme i Den Europæiske Menneskerettighedsdomstols praksis der ikke omhandler en bestemt rettighedsbestemmelse, men indeholder nogle generelt anvendelige regler der kan anvendes uafhængigt af den (mulige) krænkelse, der er til pådømmelse i den konkrete sag. Metoden opdeler domstolens citationsnetværk i forskellige retsområder og måler hver enkelt doms centralitet i dommens eget delnetværk sammenlignet med centraliteten i det samlede netværk. Domme, der er relativt mere centrale i det samlede netværk end i deres eget delnetværk antages at indeholde principper, der er relevante for alle (eller de fleste) sager uafhængigt af klagens substantielle indhold. En kontrollæsning af udvalgte domme viser, at metoden lykkes med finde den eftersøgte type domme.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".