Bibliographic record
Abstract
ueen's Park is the better for Cheri DiNovo having been a member of the Legislature.And Ontario is a better, stronger society for her participation in political life.I didn't know Cheri DiNovo at all until she arrived at Queen's Park, but from the outset I felt I knew something about her motivation because she was a United Church Minister and because I have been a member of the United Church all my life.We never talked about it early on but I felt that she was in politics for all the right reasons.It turns out that she and I were baptised in the same church, Richmond Hill United: I, when I was a toddler with my two sisters in the 1950s, she, as an adult, decades later, consciously choosing the Church to live out her activism.Cheri was elected in a by-election in September 2006, just a few days before Premier Dalton McGuinty appointed me to his Cabinet as Minister of Education.I had not been deeply engaged in the by-election but I had been in the riding of Parkdale-High Park a number of times to campaign for our Liberal candidate because this was the riding that had been vacated by Gerard Kennedy, a friend and Minister for whom I had worked as Parliamentary Assistant.We wanted to hold his riding.But Cheri was a strong, articulate candidate who won the day.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.529 | 0.418 |
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".