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Record W2806687464

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 on OpenAlexaboutno aff
Ivan Mugabi

Bibliographic record

VenueORCA Online Research @Cardiff (Cardiff University) · 2018
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMultinational corporationGovernment (linguistics)Corporate social responsibilityDeveloping countryInvestment (military)Inclusion (mineral)Economic growthEconomicsPublic relationsPolitical scienceFinanceLaw
DOInot available

Abstract

fetched live from 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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.106
GPT teacher head0.318
Teacher spread0.212 · 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.

Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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