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Record W4385350341 · doi:10.3390/youth3030059

How Businesses Can Assess the Impacts of Their Charitable Activities on the Rights of Children and Youth

2023· article· en· W4385350341 on OpenAlexaff
Tara M. Collins, Steven W. Gibson

Bibliographic record

VenueYouth · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAccountabilityCorporate social responsibilityPublic relationsHuman rightsBusinessWork (physics)RealisationFocus groupPolitical scienceMarketingLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.283
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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