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Extraterritorial reporting in mining sector: Extraterritorial reporting and global inclusion of persons with disabilities in corporate social responsibility (CSR) and corporate social investment (CSI) strategies

2018· article· en· W2806687464 sur OpenAlexaboutno aff
Ivan Mugabi

Notice bibliographique

RevueORCA Online Research @Cardiff (Cardiff University) · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueMining and Resource Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBusinessMultinational corporationGovernment (linguistics)Corporate social responsibilityDeveloping countryInvestment (military)Inclusion (mineral)Economic growthEconomicsPublic relationsPolitical scienceFinanceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The mining sector is one of the emerging multinational extraction industries in the Ugandan economy. However, the mining sector is also one of the sectors that is prone to experiencing accidents leading to long-term and 78 percent of short-term disability from time to time. In developed countries where there is greater attention for risks and a greater enjoyment of value outputs from the production chain, government/national institutions are investing resources for investigating about accidents that take place within mining sectors and resulting into injuries, temporary and permanent disabilities. For example in 2015, Canada launched a three years study, anticipated to cost about $400, 000. Its goal is to gather the information necessary to develop strategies to promote good mental health. \nWhereas in developed countries, there is a stronger compensation culture through judicial institution that give attention to small injury claims. However, in developing countries such a culture seems highly unlikely given that the reliance on employment rights seems considerably weaker. For Workers in Ontario seek disability benefits from employers for a variety of reasons. In which case mental health issues account for approximately 67 percent of long-term and 78 percent of short-term disability claims in Canada. Even though similar concerns of long terms and short-term disabilities are highly likely to subsist among workers in Uganda’s emerging mining sector, there is hardly much evidence of policies encouraging employee to seek compensation through work related disability benefits from their employers. Even then, in developing economies idea of outsourcing production and those of foreign direct investments (FDI) have in come context detach the ultimate manufacturers. \nAs a result, workers who would be more legally protected as employees are independent contractors are without much support in case of becoming persons with disabilities due to work related injuries during mining. In light of the above, this study shall uses lenses of corporate social responsibility (CSR) and Corporate Social investment (CSI) in advancing a feature possibility for new means of extraterritorial reporting by some western mining companies in Uganda. In this way, the paper is supporting the extension of CSR and CSI reporting obligations to include workers with disabilities as stakeholders in their chain of production. It is imperative to note that the above accountability for disability support should arise from the circumstances underlying relationships arising from the chain of production. In which case the direct presence or absence of employer-employee relationship become less important than the presence of a humanly disabling supplying chain from which the producer is securing raw materials. Thus, CSR and CSI will encourage producers that are geopolitically detached from developing countries such disabilities are occurring to participate in skills development that include persons with disabilities all their training programmes. Hence creating opportunities within mining organizations of developing economies where extraction that are inclusive and accommodative to both new employees and learners with disabilities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,042
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,083

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,042
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0040,005
Études des sciences et des technologies0,0010,002
Communication savante0,0050,008
Science ouverte0,0010,008
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,106
Tête enseignante GPT0,318
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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