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Record W2089292226 · doi:10.1108/14777830610702557

Examining waste management in San Pablo del Lago, Ecuador: a behavioral framework

2006· article· en· W2089292226 on OpenAlexaff
May Aung, Martha L. Arias

Bibliographic record

VenueManagement of Environmental Quality An International Journal · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMultidisciplinary approachOriginalityGarbageValue (mathematics)Empirical researchClass (philosophy)PsychologySociologyQualitative researchSocial scienceEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of the paper is to propose and examine with evidence from Ecuador a behavioral framework that helps understand environmental practices in a small rural community. Design/methodology/approach This research is a multidisciplinary study that integrates ethnographic, feminist, and fourth generation approaches. Qualitative and quantitative methods were applied. Findings Findings indicate a number of relevant determinant factors (social norms, personal norms, intention to act), moderating factors (knowledge of the issues, awareness of the consequences, knowledge of the strategies and action skills, assumption of the responsibilities), and socio‐demographic factors (gender and social class) that influence solid waste (garbage) management behavior in a small rural community in the Ecuadorian Andes. Practical implications This study recommends general public training for the stakeholders of this community taking into account gender and social class differences. The importance of generating role models in groups such as business owners and teachers to lead in waste management behavior is also suggested. Originality/value This study develops a behavioral framework with supporting empirical evidence from Ecuador that aids the understanding of environmental management practices of women and men from a small cohesive community

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.030
GPT teacher head0.324
Teacher spread0.294 · 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 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

Citations22
Published2006
Admission routes1
Has abstractyes

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