The language of the law vs. the language of the computer: a bilingual model of legal education in the age of technology and artificial intelligence
Bibliographic record
Abstract
The traditional legal education does not equip students with the necessary skills to address the challenges presented by technological advancement and artificial intelligence. Moreover, current attempts to address these challenges fall short of achieving their desired objectives.I argue that the only viable solution to this issue is for law schools to transition from being monolingual to bilingual. At present, law schools are monolingual in the sense that they solely teach ‘the language of the law.’ This language is inherently ambiguous and vague, has a multivalent relationship with truth, and requires a particular way of thinking – ‘thinking like a lawyer.’ To effectively address the challenges presented by technology and survive in the age of artificial intelligence, it is imperative for law schools to become bilingual and incorporate the teaching of ‘the language of the computer.’ In contrast to the language of the law, the language of the computer is clear and precise, has a bivalent relationship with truth, and requires a different way of thinking – ‘thinking like a computer.’ To ensure their survival, law schools must swiftly integrate the language of the computer into their curriculum to pioneer the algorithmic evolution of law within the legal field.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".