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Record W2340430218 · doi:10.1177/1359183515622966

Redefining pollution and action: The matter of plastics

2015· article· en· W2340430218 on OpenAlexaff
Max Liboiron

Bibliographic record

VenueJournal of Material Culture · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPollutionAction (physics)AnthropoceneAgency (philosophy)Environmental ethicsScale (ratio)SociologySocial scienceGeographyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Using plastic pollution as a case study, this article shows how the material characteristics of objects – their density, their size, and the strength of their molecular bonds, among other traits – are central to their agency. The author argues that it is crucial to attend to the physical characteristics of matter if we, as researchers, are going to describe problems and contribute to solutions for ‘bad actors’ like pollutants. Plastics and their chemicals are challenging regulatory models of pollution, research methods, and modes of action because of their ubiquity, longevity, and scale of production. This article investigates how scientists researching plastic pollution are attempting to create a new model – or models – of pollution that account for the unpredictable and complex materialities of 21st-century pollutants, and how the Anthropocene has come to be a shorthand for our material understandings of moral transgressions, cherished boundaries, and good citizenship.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0120.074
Scholarly communication0.0110.011
Open science0.0020.012
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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 designTheoretical or conceptual
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

Citations262
Published2015
Admission routes1
Has abstractyes

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