Bibliographic record
Abstract
Given increasing criticism and dropping admissions, American legal education is likely to change, hopefully reversing the unsustainable trend of increasing expense without increasing value. Much debate focuses on restructuring the curriculum to make it more “practical” and skills-infused; here we instead propose a rethinking of the basic unit of law teaching, the casebook. Casebook authors and publishers are cautiously venturing into electronic editions, but they fail to harness the power of social learning to make textbooks dramatically smarter as well as cheaper. We are developing an online platform that reinvents both authorship and learning. The platform, which has progressed to alpha testing, provides an online system for crowdsourcing authorship by law professors (including shared and socially ranked case selections, edits, annotations, questions, and problems) and reading by law students and others (including shared and socially ranked highlights, notes, questions, answers, and other interactions, as well as live collaboration). Rather than settle for twentieth century casebooks in digital form, we aim to enable twentyfirst century coursebooks that originate in, and then grow increasingly useful and valuable through, social intelligence.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.033 |
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".