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Record W4312996522 · doi:10.46692/9781529206586.002

The Public Problem of Waste

2022· other· en· W4312996522 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsWaste managementBusinessEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

We are nowhere near ‘peak waste.’ Hoornweg et al, 2014: 117 When we read news about waste – plastic bags clogging water drains in India and causing contaminated drinking water, child labour used to dismantle used electronics in Malaysia, space junk orbiting earth or PPE masks washing up on our shores – our waste problem seems clear: waste is being mismanaged! The solution appears similarly obvious: we must better manage our waste! But the devil, as the famous idiom goes, is in the details. What we understand the problem to be – the mismanagement of waste – depends on how we actually define waste. Whose waste are we referring to? Who should be responsible for managing it better? And what, more specifically, would register as ‘better’ waste management? At first glance, the definition of waste seems equally obvious. Waste is all of that stuff that we once wanted but no longer want (Strasser, 1999). Waste is all of those things we put in our trash can, and if our local waste services are functioning well, are whisked away from our homes to quickly become out-of-sight and out-of-mind. Yet, waste turns out to be a rather complex problem involving different rightsholders and stakeholders, temporalities, geographies, political economies, transnational agreements, regulations and policies, and cultural traditions that disproportionately affect a range of publics. To introduce this complexity, let’s consider the following waste snapshots: Snapshot 1 The Republic of the Marshall Islands is a United States associated state and comprises some 1,156 islands in the Pacific Ocean, north of New Zealand. With a total population of just over 58,000, most of its territory (over 97 per cent) is water. Beginning around the 10th century, successive waves of colonizers and settler colonizers claimed the islands, from Micronesians, to Spanish, to German, to Japanese, and finally to Americans during World War II. The US began nuclear bomb testing on the Marshall Islands’ Bikini Atoll in 1946 and continued detonating nuclear arms for over a decade. During this period, the US exploded 23 nuclear weapons, first above-ground and then underground. The second – Baker test – detonation contaminated all of the surrounding ships, leading Glenn T. Seaborg, chair of the Atomic Energy Commission, to call it “the world’s first nuclear disaster” (in Weisgall, 1994: ix).

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)0.011
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0170.047
Scholarly communication0.0220.033
Open science0.0020.020
Research integrity0.0160.019
Insufficient payload (model declined to judge)0.0220.004

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 head0.011
GPT teacher head0.209
Teacher spread0.199 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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