The Chimera of the Real and Substantial Connection Test
Bibliographic record
Abstract
This paper was first presented at a symposium held at the University of British Columbia Faculty of Law on November 5 and 6, 2004 to honour the late Mr. Justice Kenneth Lysyk, a former faculty member and Dean of Law at U.B.C. For this paper we chose a topic that combines both of Ken Lysyk's favourite subjects. We set out to examine how the Supreme Court of Canada has used the "real and substantial connection" test in the conflict of laws and in related areas of constitutional law. This test has been adopted for a variety of purposes. We suggest that it serves some of these purposes better than others. In addition, we suggest that the test, as it is presently structured, serves none of its purposes especially well. The law makes frequent use of criteria that turn on an overall appreciation of a variety of factual elements. The law cannot do without such criteria, and often they function as the core concept for an area of law. The "real and substantial connection" test is such test (or actually, we would argue, several such tests using the same verbal formula for distinct purposes). We contend that the test does its job, or jobs, less well primarily because the standpoint from which the evaluation is performed is much less well-defined than it is in the others. A secondary reason is that the variety of contexts in which the test is employed makes its underlying rationale even harder to pin down.
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 | 0.021 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".