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Record W1979376974 · doi:10.1680/warm.13.00011

Briefing: Social facets of solid waste: insights from the global south

2013· article· en· W1979376974 on OpenAlexaff
Jutta Gutberlet

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Waste and Resource Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMunicipal solid wasteCleaner productionBusinessCommodityConsumption (sociology)Production (economics)Waste managementResource (disambiguation)Environmental planningEnvironmental economicsEngineeringEconomicsSociologyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Most commonly municipal solid waste is being treated from an engineering and technological perspective only and is either perceived as a nuisance or a commodity, while the social facets permeating waste issues are less prominent in this debate. Conceptualising waste as being worthless and yet also a coveted resource reveals a central contradiction affecting waste, which surfaces in solid waste management decision making. The complexity of current waste problems requires an integrated, multifaceted and interdisciplinary approach that is aware of the social side of materials. Production, consumption and lifestyle habits generate waste, which is part of the current, global environmental crisis. Reduction and recovery of recyclable materials address the serious ecological ‘overshoot’ concern of this crisis. Informal but organised recycling in Brazil is discussed as an innovative form of an inclusive resource recovery and environmental awareness strategy. Public policies need to safeguard the social dimension in addition to the ecological and economic aspects in waste management.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0070.006
Scholarly communication0.0060.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 designQualitative
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

Citations26
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Waste and Resource ManagementSame topicMunicipal Solid Waste ManagementFrench-language works237,207