Future-Oriented Civil and Private Law: Integrating Artificial Intelligence (AI) and Machine Learning (ML) Technologies
Bibliographic record
Abstract
Abstract A recent study delved into the burgeoning fields of artificial intelligence (AI) and machine learning (ML) within civil and private law. The study, which focused on analyzing 140 academic papers published from 2017 to 2023 using software tools such as RStudio, VOSviewer, and Excel, revealed a significant rise in publications, particularly since 2020. Key research topics have examined the application of AI in decision-making on civil and private law, argument, persuasion and progress in the legal processing of natural language through deep learning techniques. The study also identified leading contributions from scientists in the US, Canada and the Netherlands. However, it also recognizes the need for further research to evaluate the effectiveness of various AI and ml methodologies in legal contexts. This in turn would support civil and private evidence-based rights in various sectors and geographical regions, which would increase their usability and integration.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".