Bibliographic record
Abstract
During the COVID-19 pandemic, threats to children’s well-being and health in Canada have been heightened, while children’s voices have not been granted sufficient attention. Research conducted by Children First Canada (CFC), a charitable organization that serves the over 8 million children living in Canada, over the past four years has continually highlighted the top ten threats to childhood in Raising Canada reports and provided calls to action to mitigate and respond to these concerns. In the Raising Canada 2021 report, cross-cutting themes present across the top threats were also described--one of which touched on the ways in which children and youth often lack sufficient prioritization and inclusion in public policies. Despite these findings, CFC has taken purposeful action to ensure children are granted spaces to participate and inform policy related discussions--internal to the organization or in public spheres. As such, in this paper we highlight these threats and the overarching theme on the lack of engagement with and prioritization of young people, we describe three key ways that CFC has actively included young people, and we describe why inclusion is important for social change. We also discuss what strategies for inclusion can look like in post-pandemic times, but emphasize the need to have inclusion of young people engrained in the fabric of a functioning society.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.045 | 0.059 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".