Children’s human rights under COVID-19: learning from children’s rights impact assessments
Bibliographic record
Abstract
Policy responses to COVID-19 have had dramatic impacts on children’s human rights, as much as the COVID-19 pandemic itself. In the rush to protect the human right of survival and development, new policies and their implementation magnified the challenges of taking a children’s rights approach in adult-oriented systems and institutions. This article explores these challenges, drawing on learning from the independent Children’s Rights Impact Assessment (CRIA) on policies affecting children in Scotland during ‘lockdown’ in spring 2020. The article uses concepts from childhood studies and legal philosophy to highlight issues for children’s human rights, in such areas as children in conflict with the law, domestic abuse, poverty and digital exclusion. The analysis uncovers how persistent constructions of children as vulnerable and best protected in their families led to systematic disadvantages for certain groups of children and failed to address all of children’s human rights to protection, provision and participation. The independent CRIA illuminates gaps in rights’ accountability, such as the lack of children’s rights indicators and disaggregated data, children’s inadequate access to complaints and justice, and the need for improved information to and participation of children.
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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 | 0.051 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".