Some Considerations for Child Rights Impact Assessment (CRIAs) of Business
Bibliographic record
Abstract
With increasing international attention to business and human/child rights, the necessary next step must examine the monitoring of activities in order for the connection between business and child rights to be meaningful. Consequently, the essential question for this paper is whether business should use child rights impact assessments (CRIAs) and if so, what are some considerations in order to move forward? It is argued that the business must develop and carry out CRIAs in order to meet its due diligence obligations, and to identify and respond to potential and actual child rights impacts due to business activities. Business should use CRIAs as part of a broader process of supporting its organizational commitment to human rights. Other actors can also participate in this monitoring endeavour of business activities. This paper identifies some challenges and considerations of CRIAs in relation to business.
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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 | 0.087 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.021 | 0.018 |
| 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".