Guest Speaker: Osgoode Alum LLB'90 Evan Siddal, President & CEO, Canada Mortgage and Housing Corp.
Bibliographic record
Abstract
On November 15, 2018, in conversation with Mary Condon, Dean (Interim), Evan Siddall discusses his career journey from Wall and Bay Streets to public service, prompted by a desire to contribute to a greater cause. He speaks to the value that diversity of thought and experience can bring to the public service. He also addresses the challenge that “tall poppy syndrome” poses to attracting the best and brightest and how it threatens Canada’s capacity for innovation at a time when it’s needed most.\nAbout the speaker: Evan Siddall and his executive team have transformed the Canada Mortgage and Housing Corporation (CMHC) into a high-performing, innovative organization with a vision of being at the heart of a world-leading housing system. Under Evan’s direction, CMHC is leading Canada’s first-ever National Housing Strategy, an ambitious $40+ billion, 10-year plan to reduce housing need in Canada.\nEvan has worked at some of the world’s largest investment banking firms in Canada and the United States before joining the Bank. But a formative visit to Canadian battlefields and Vimy Ridge in northern France motivated him to consider public service. In 2010, he was urged by former colleague Mark Carney to join the Bank of Canada. Taking lessons from the global financial crisis of 2008 and his career in finance, Evan spearheaded the Bank’s efforts in establishing financial infrastructure to protect the Canadian economy against future risks.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.223 | 0.082 |
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".