Preserving the Integrity of Financial Markets in North America - Canadian Speaker
Bibliographic record
Abstract
Speaker I feel a little bit overwhelmed by this group of people, professional lawyers with deep experience, and people in the policy information area.Those of you who are Canadian will recognize that the Canadian bankers do not have a very good track record of success in the policy area.In areas pertaining to things that are near and dear to us like bank mergers, we have been singly unsuccessful in getting any effective policy.That also goes for a whole series of things that we as bankers have been advocating.So, it is with some trepidation that I stand here in front of you to talk about policy.It kind of reminds me of that pair of balloonists who were floating over the Californian hills on a lovely sunny summer's day, like we are going to have in a couple of weeks' time.They had gone through their champagne, and they kind of lost track.They noticed when they looked over the basket that the clouds had come in, and they did not know where they were.So they let some gas out and floated down.As they came through the clouds, there on the hill was a solitary man walking.They called down to him, "Can you tell us where we are?"He looked up."You are in a balloon."The one balloonist said to the other, "That man down there, he is a banker."The other guy was a little taken back by this and said, "How do you know he is a banker?"He replied without thinking, "What he said was totally accurate, and it was absolutely irrelevant."With that, I will talk a little bit about some of the things that I have seen in both the U.S. jurisdictions and the Canadian about integrity and financial markets in North America.I should probably begin by squaring the title, which talks about preserving, because really what we have seen is quite a John F.M. Crean is the Senior Executive Vice President for Credit and Risk Management at The Bank of Nova Scotia.He is responsible for centralized risk control function within the Bank.Prior to his current appointment, Mr. Crean has held a number of appointments in the Bank, including those of an Executive Vice President, Corporate Banking for the central regions of North America, and Vice President and General Manager of the Toronto Suburban Region, and between 1975 and 1979, he was General Manager, Systems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 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".