Bibliographic record
Abstract
Many countries have an ever-widening access-to-justice gap and lawyers continue to have disproportionately high rates of addiction, depression and suicide; legal technology is one way to fix these problems, making its creation more purposeful than generating money and doing cool things. LegalTech enthusiasts gather monthly in an ever-growing number of cities around the world. Every meeting fertilizes new ideas on how to “fix” legal services. And “in need of repair” is very much how the inhabitants of the LegalTech world see legal services. They view law as nothing but code and decision trees (if this, then that), and they pay little heed to tradition. The ultimate legacy of legal technology will not be AI-powered lawyer robots on the blockchain, but rather the transformation of legal services from a lawyer-dominated industry into a service fuelled by a human-technology combo that is merely augmented by lawyers.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.027 |
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".