How Businesses Can Assess the Impacts of Their Charitable Activities on the Rights of Children and Youth
Bibliographic record
Abstract
There has been increasing attention given to the relationship between children’s rights and business due to a greater understanding of the direct and indirect impacts that businesses have on children and youth. Concomitantly, many businesses are involved in charitable work. Do charitable activities performed by businesses provide an entry point for considering children’s rights? Further, do these charitable activities facilitate an opportunity for the amalgamation of corporate social responsibility (CSR) and business and human rights? It is hypothesised that charitable contributions can facilitate a greater understanding of children’s rights and subsequently advance implementation. Accordingly, businesses can recognise their capacity to do more than mitigate their negative impacts, and positively influence the realisation of children’s rights. This can be facilitated through the improved assessment of charitable contributions using a child-rights-based approach. This research paper is informed by qualitative individual interviews with 15 stakeholders from pertinent professional sectors, five focus groups with 38 children and youth, and academic and grey literature reviews. It is concluded that attention to impact assessment offers a valuable avenue forward by which to knit the threads of activity regarding both human rights and corporate social responsibility through accountability. A proposed checklist may stimulate future actions and developments in children’s rights within and outside of businesses.
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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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".