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Record W2151401867 · doi:10.1177/0270467606295193

A Description and Analysis of the German Packaging Take-Back System

2006· article· en· W2151401867 on OpenAlexaff
Nina Nakajima, Willem H. Vanderburg

Bibliographic record

VenueBulletin of Science Technology & Society · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural safety and regulations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAllowance (engineering)BusinessGermanProduct (mathematics)Packaging and labelingRaw materialWaste managementCommerceEnvironmental economicsEngineeringOperations managementMarketingEconomics

Abstract

fetched live from OpenAlex

The German packaging ordinance is an example of legislated extended producer responsibility (also known as product take-back). Consumers can leave packaging with retailers, and packagers are required to pay for their recycling and disposal. It can be considered to be successful in reducing waste, spurring the redesign of packaging to be more environmentally sustainable, and increasing refilling and recycling. The exception is waste packaging made of plastics, which faces the problems of export due to lack of markets for recycled products within Germany, the lack of capacity for recycling, the allowance for energy recovery within the ordinance, and the manufacturers’ focus on back-to-feedstock rather than back-to-plastics as a solution. This article looks at the ordinance and its effects to consider why this take-back program worked better than others.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.008

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 head0.008
GPT teacher head0.190
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2006
Admission routes1
Has abstractyes

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