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Record W2153959478 · doi:10.21083/surg.v3i1.1025

Bad aim: Separating the intentions and effects of American newsprint recycling legislation on landfill space, forest conservation and Canadian newsprint producers

2009· article· en· W2153959478 on OpenAlexaffvenueabout
Joshua Nasielski

Bibliographic record

VenueSURG Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNewsprintHarmLegislationBusinessDumpingGovernment (linguistics)Natural resource economicsLawEconomicsPulp and paper industryEngineeringInternational tradePolitical scienceKraft paper

Abstract

fetched live from OpenAlex

This paper argues that the American newsprint recycling laws passed during the late 1980s and early 1990s not only failed to achieve their stated environmental objectives, but failed so spectacularly that they actually contributed to further environmental harm. These laws, which imposed a recycled content standard on new newsprint production, had three intentions: to decrease landfill space requirements, to preserve forests, and to encourage the recycling of used newsprint. Insofar as the first two intentions are concerned, this paper finds that the American newsprint recycling laws had a negligible effect on both landfill space and forest conservation. But by succeeding in elevating the amount of newsprint recycling far beyond what it would otherwise be, industry compliance with these laws may have actually increased environmental harm. From a Canadian perspective, these laws essentially encouraged Canadian newsprint producers to import American newsprint waste. In addition, by imposing compliance costs on American and Canadian newsprint producers in the range of several billions of dollars, these laws prevented capital from being productively spent on other environmental initiatives. Surveying alternatives to government mandated recycling legislation, this paper ends by concluding that if governments wish to improve environmental outcomes through newsprint recycling, their best option may be to extend the functioning of markets.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.285
Teacher spread0.263 · 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.

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

Citations0
Published2009
Admission routes3
Has abstractyes

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