Logic, Probability Theory, and their Application to Legal Reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both the use of logic and probability theory are heavily applied in many, if not all, areas and industries due to what they can offer. Logic is well known for enabling one to comprehend many things which include intentions, behavior, beliefs, intelligence, knowledge and languages, and to create algorithms designed to solve simple and complex problems. Probability theory is specifically designed to address uncertainty, which uncertainty exists everywhere. Through pairing probability theory with other disciplines is one able to address uncertainty in other fields. Combined, logic and probability theory enable one to address complex problems in law. This paper will first describe logic and probability theory, then address their application to legal reasoning. Accordingly, I argue that logic and probability theory can be used to validate thoughts and arguments made by lawyers and judges, allowing for better understanding of the validity, coherency, truthfulness of their arguments, and the correctness of the statements and decisions made by them.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it