Strategic Decisions in Setting Up Child Rights Impact Assessments
Bibliographic record
Abstract
Establishing a child rights impact assessment (CRIA) requires making strategic choices in regards to scope and process. This article focuses on choices regarding (i) the material scope: for which documents will a CRIA be carried out?; (ii) the personal scope: will a CRIA be done only for children (0-18 years), or be extended towards young adults?; and (iii) the relationship to other instruments and processes: will a CRIA stand alone, or be integrated with other impact assessments? The relation between CRIA and other instruments, such as child(-friendly) budgeting is also discussed. The article illustrates these choices, drawing on an evaluation of the experience of implementing the Child and Youth Impact Report (JoKER) in Flanders (Belgium).
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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 | 0.325 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".