Bibliographic record
Abstract
The Honourable Landon Pearson's domestic and global advocacy efforts with, for, and on behalf of children and young people have unfolded over a period of sixty years including thirty years in the Canadian Foreign Service and eleven years in the Senate of Canada. Two of the key ideas that frame her vision are that as rights holders, children have a right to participate in matters that affect their lives, and that every child needs at least one adult to provide steadfast and consistent support. In The Children's Senator contributors detail Pearson's influence on children's rights scholarship, research, and advocacy in a variety of areas including Indigenous children's rights, youth justice, commercial sexual exploitation of children, children's mental health, and corporal punishment. Following Pearson's lifelong commitment to highlighting young people's participation, the volume also includes testimonials from former students regarding her invaluable mentorship. Pearson's professional career and aspects of her personal life, including her experience as a parent of five children, merge in a fascinating account of Canada's premier children's rights advocate. An intimate and compelling collection, The Children's Senator celebrates Pearson as a catalyst of change in Canada and internationally. Her efforts to construct a children's rights architecture in collaboration with decision-makers and young people inform a legacy that has laid a foundation for children's rights into the twenty-first century.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".