Assessing barriers to reuse of electrical and electronic equipment, a UK perspective
Bibliographic record
Abstract
This paper reports on research undertaken to identify generic and specific barriers to reuse of electrical and electronic equipment (EEE). Thirty semi-structured interviews were conducted with experts from across the value chain including product designers, manufacturers, users and waste managers as well policy makers and academics. The interviews sought to examine perceived and real barriers to reuse in the UK. Three inter-connected factors that limit opportunities and instances of reuse of electrical and electronic equipment were identified, highlighting that both systemic and consumer barriers to increasing levels of reuse exist. These are: producer reluctance, unsuitable collection infrastructure and cultural issues. Overall, the paper shows that low levels of reuse in the electrical and electronic sector are a result of complex and interlinked barriers. Understanding these connections offers the potential to improve the opportunities for reuse, by providing direction for policy makers to address barriers from a multi stakeholder perspective. Increasing instances of reuse is essential if the UK is to successfully move towards a resource efficient, circular economy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".