Bibliographic record
Abstract
hank you Dick, for your generous introduction.There is just one thing I have to correct.When you said that I was in the law office in the afternoon, I would say that it was most afternoons.Some afternoons were in the pool hall.It is great to be home and it is a singular honour to have been given the opportunity to participate in the Isaac Pitblado Lectures on the 100th Anniversary of the Manitoba Court of Appeal.I know all the members of the Court and I go back a long way with the old ones.My relationship with each of them, of course, is different.Guy Kroft, who just retired this week, and I know each other best because we were plaintiffs together.We had invested in a limited partnership and made a profit, unusual for me, and declared the profit as a capital gain.The Government said it was income and reassessed us.Guy was a judge at the time and I was still a lawyer.When it came time for examinations for discovery, we had to decide who would be discovered.Guy announced that it would have to be me because it would be unseemly for a judge to be examined.All I can say is, it's easier to ask the questions than to answer them.Charlie Huband taught me trust law.Charlie looks the same today as he did 43 years ago.It's the hair -a sensitive subject for me.I litigated with Dick Scott and Kerr Twaddle.They always won.Sometimes I was bitter about it.But I certainly don't want anyone to think that now it's payback time.And, of course, Martin Freedman and I were partners and close friends for many years at Aikins, MacAulay & Thorvaldson.So those are the old guys.Now, Barb Hamilton was also a partner of mine at Aikins and a good friend.But she was much younger.Freda Steel, I have gotten to know quite well over the years since we both became judges.And Mitch Monnin and I know each other but mostly vicariously because Marc Monnin and I worked together in the Transportation Law Department at Aikins and Marc would complain to Mitch about how hard I was to work with.Well, I know I have engaged in name-dropping but
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 | 0.053 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.102 | 0.069 |
| Insufficient payload (model declined to judge) | 0.083 | 0.020 |
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".