Bibliographic record
Abstract
Students in first year law in English-speaking common law schools in Canada follow a fairly standard curficulum, heavily weighted in favour of private law subjects such as torts, contracts and property, with criminal law, constitutional law, and perhaps a methods, theories or skills course rounding out their required courses. Most students find the content to be as they expected in courses in torts, contracts, criminal and constitutional law. These areas of law, after all, provide the law-related stories that are an increasing part ofnational and even international news. But many students find first year property a puzzle. They expect the course to deal with protecting one's property from others; buying and selling land; creating and marketing residential, commercial and recreational developments; or raising money on the security of land. Instead, students are presented with ideas of property rights and obligations that developed in England in the five hundred years following the Norman Conquest. Grappling with such abstractions as freehold and leasehold estates, reversions and remainders, or determinable and defeasible interests, students have little time to ponder why rules that developed in a very different time and place continue to shape the possibilities for structuring their client's business and family opportunities in the twenty-first century.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".