Bibliographic record
Abstract
In 1989, Canada played a prominent role in helping the international community draft the United Nations Convention on the Rights of the Child (UNCRC). Eighteen years after Canada ratified the UNCRC, a 2007 United Nations Children’s Fund (UNICEF) report argued that relative to other nations on the list of the world’s 21 richest countries, Canada has been slow to honour its commitment to uphold these rights and ensure the well-being of children (Canada ranked 12th on the list, and the United Kingdom and the United States ranked 20th and 21st, respectively). The report singled out the plight of Aboriginal children as especially desperate, noting that in some communities they lack access to adequate housing and education, and even clean water (UNICEF 2007).2 Although the Government of Canada promised to improve conditions in its 1997 Gathering Strength: Canada’s Aboriginal Action Plan (Minister of Indian Affairs and Northern Development 1997), there is still no legal framework and no independent national children’s commissioner to monitor implementation of children’s rights federally and to coordinate federal, provincial and territorial policies that affect children. These needed strategies were recommended in a 2007 Senate report (Canada, Standing Senate Committee on Human Rights 2007).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".