Children's Rights: Building From a Sense of Fairness. An Interview with Landon Pearson
Bibliographic record
Abstract
This year marks the 25th anniversary of the adoption of the United Nations Convention on the Rights of the Child (UNCRC). The most widely endorsed human rights instrument, the UNCRC represents a key legislative and advocacy tool to promote and protect children’s rights and, in so doing, to change attitudes towards childhood. Despite the many challenges that remain, children’s rights have transformed our way of looking at children and how children are treated around the world. In Canada, this time period is intimately tied to the figure of Landon Pearson, former Canadian Senator (1994-2005), founder of the Landon Pearson Resource Centre for the Study of Childhood & Children's Rights at Carleton University, and a tireless advocate for children’s rights. In this interview with McGill Professor Mónica Ruiz-Casares, Landon Pearson traces back her interest in children’s rights and children’s voice, and reflects on the UNCRC—the context in which it emerged, its achievements and shortcomings to date, and the opportunities and challenges ahead. She shares her views on leadership and provides advice to researchers and practitioners to advance children’s rights into the future.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".