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Record W7128540285 · doi:10.64903/1480-6800-25.2.154

Legal Problems of Electronic Pollution According to UAE and Egyptian Law

2022· article· W7128540285 on OpenAlexvenueno aff
Ahmed Moustafa Aldabousi

Bibliographic record

VenueArab world geographer · 2022
Typearticle
Language
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationElectronic wasteLiabilityContext (archaeology)HarmDumpingDeveloping countryHazardous waste

Abstract

fetched live from OpenAlex

Electronic waste (E-waste) is currently deemed to be one of the most challenging greatest problems causing harm to the world due to its health and environmental hazards. Its toxic accumulation is also due to the difficulty of disposing of it or recycling some of its components, which represents a challenge to the organizations concerned with human health and a clean environment both in the industrialized and world and in developing countries. Concomitantly, it also represents a legal challenge as well, calling forth a need to pass legislation to oversee and control electronic waste. Electronic pollution has become a serious danger in developing world countries because such states are often targeted as dumping spaces for the export of used electronic devices and the associated waste thereof, part of the policies and geography of material waste disposal, in the US, the Middle East and elsewhere (see also: Nyamrunda2o20; EPA 2022a; EPA 2022b; De Oliva 2021; CEC 2016; Wang et al. 2016). Within the context of Egyptian and the UAE legislation, this paper examines the problem of electronic waste pollution as well as its related hazards and what can and may result therefrom, such as the associated problematic legal issues. It addresses the definition of electronic waste, the necessary sound management of thereof and the role of the law in this regard, and seeks to explain the liability elements arising from the electronic pollution as a global challenge.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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