The Law of the Land: The Advent of the Torrens System in Canada by Greg Taylor
Bibliographic record
Abstract
As a former history student and erstwhile professor of first-year property, The Law of the Land: The Advent of the Torrens System in Canada by Greg Taylor offered me an opportunity to engage in academic reading with just a hint of “night table book” indulgence. As the title suggests, the author carefully traces the development of the Torrens registration system in Australia and its subsequent reception within Canada — an undertaking that even those within the legal profession may find as dry as the dust on the land titles records. However laborious the research, the book itself manages to bring history to life; chasing clues from Adelaide to the United Kingdom National Archives to the records of the Toronto Globe in an attempt to divine the motives and influences of the Torrens prophets and their opponents. The result is an original and interesting account incorporating law and politics spanning some 150 years of Canadian history.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".