Bibliographic record
Abstract
Children’s wellbeing has moved from an academic field of interest to a policy and practice framework, internationally and in many countries. Children’s wellbeing tends to be twinned casually with children’s rights but recent Scottish legislation – the Children and Young People (Scotland) Act 2014 – has put children’s rights and children’s wellbeing in tension. This provides an opportunity to consider the concepts critically. The article scrutinises parliamentary debates and accompanying submissions, to reveal that children’s rights arguments failed due to political concerns about litigation and a lack of evidence that children’s rights improved children’s lives. Children’s wellbeing arguments were more successful, as children’s wellbeing continues the familiar trajectory of a needs-based approach. It has additional benefits of maximising outcomes, emphasising early intervention and prevention, and statistical development. It also risks being apolitical and professionally-driven, with no minimum standards and limited recourse for children’s and their families’ rights and complaints. These findings raise broader questions about how to argue for children’s rights in national and global contexts where children’s wellbeing has ever-increasing prominence, fuelled by calls for evidence-based policy and accountability via outcomes.
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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".