Legal Problems of Electronic Pollution According to UAE and Egyptian Law
Bibliographic record
Abstract
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".