Notes from the Field: E-waste in Brasil - Lixo Eletrônico and MetaReciclagem
Bibliographic record
Abstract
As probably every other country in the world, in recent years Brazil has seen an immense increase in the production and consumption of electro-electronics equipment, which generates, as expected, an equally large amount of e-waste. However, there is a general lack of information about health and environmental issues among actors involved with the e-waste cycle, and very limited public discussion about the topic. Also, proper legislation to regulate the destination of all this material does not yet exist. The National Policy on Solid Residuals, which has been discussed in the Brazilian Congress since 1991, had shown signs of including ewaste management. But the responsible working group in the Chamber of Deputies has recently decided to make an amendment to its 33rd article, dealing with the regulation of reverse logistics (take-back) and mandatory recycling of special waste, and no longer considering electronic equipment as such. In response to that, the collective Lixo Eletrônico decided to publish a manifesto and open an online petition drawing attention to the change in the bill. The article offers an overview of the situation and the issues involved and explains the action that is being carried out by the Lixo Eletrônico Collective, presenting the first outcomes and next steps.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".