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Record W4232351529 · doi:10.24908/iqurcp.8985

Informal Recycling In Developing Nations

2016· article· en· W4232351529 on OpenAlexvenueno aff
Amanda Hart

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsGarbageInformal sectorBusinesssortDeveloping countryNatural resource economicsEnvironmental economicsWaste managementEconomic growthEconomicsEngineeringComputer science

Abstract

fetched live from OpenAlex

The topic of my research is informal recycling with a focus on developing nations. Scavengers are considered people who sort through garbage but not through an organization. There is a negative stigma that is associated with this type of lifestyle. The discussion will explore the benefits of organized informal recycling programs in countries such as Brazil and Nigeria. When informal recycling becomes organized jobs are created allowing for more residents to become employed. Some of the benefits of informal recycling include reducing the volume of waste, the life span of disposal sites is increased as well it helps reduce the amount of methane produced. These programs also allow for certain materials to be discovered which can easily be reused. For example, there are metals that can be sorted through and ultimately sold to companies. Another example would be the organics from the garbage are used in order to support pig farms. This decreases the cost of production for the pig farmers, which allows them a larger profit margin. Also, social, economic, environmental and health issues will be discussed in further detail. Finally, terms will be defined to allow a better understanding of the informal recycling world and how it operates.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.114
GPT teacher head0.369
Teacher spread0.255 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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