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Record W4362608417 · doi:10.30958/ajbe.9-2-7

E-waste Awareness Among Young Generation

2023· article· en· W4362608417 on OpenAlexaff
Nazlı Ölmez, Türker Baş, Aslı Gül Öncel, Michel Plaisent, Prosper Bernard

Bibliographic record

VenueAthens Journal of Business & Economics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsContext (archaeology)BusinessPhoneMobile phoneElectronic wasteEnvironmental pollutionEnvironmental economicsNatural resourceMunicipal solid wastePanoramaPollutionWaste managementEnvironmental planningEnvironmental scienceEngineeringEnvironmental protectionComputer scienceGeographyPolitical scienceEconomicsTelecommunicationsEcology

Abstract

fetched live from OpenAlex

Environmental pollution is becoming a high-priority concern, as it threatens the natural resources of many countries. In this context, electronic waste (e-waste) pollution is expected to play an important role in the ecosystem. E-waste is an emerging type of pollutant, defined as the various forms of electrical and electronic material that have stopped being of value to their users or no longer satisfy their original purpose. In our study, the aim was to measure the awareness of young generations regarding the e-waste concept and to analyze how much young people are familiar with the regaining activities. To achieve the goal of this study, an e-waste survey of 9 multiple choice questions was formed in an online survey platform. This paper provides a panorama of the awareness, actual motives for change and disposal method, attitudes and other factors potentially explaining the intention to recycle, and deterrents to recycling. Keywords: e-waste, recycling, mobile phone, environment, statistics

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.233
Teacher spread0.206 · 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 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
Published2023
Admission routes1
Has abstractyes

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Same venueAthens Journal of Business & EconomicsSame topicRecycling and Waste Management TechniquesFrench-language works237,207