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Record W4399379511 · doi:10.15353/joci.v20i1.5289

Youth-Driven, Community-Engaged Waste Management

2024· article· en· W4399379511 on OpenAlexvenueno aff
Nova Ahmed, A. K. M. Q. E. Khuda, Sayema Hussain Chowdhury, Tahira Rezwana, Md. Sharif- Ul Islam, Sumiya Sajjad

Bibliographic record

VenueThe Journal of Community Informatics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSociology

Abstract

fetched live from OpenAlex

The waste management process is important in Bangladesh where infectious and non-infectious diseases are common. In a resource constrained region, community engagement for waste management can add great value. A youth-initiated and engaged approach to collaboration among local communities is presented in this research. This research took place over a one and half year time frame in fifteen urban, suburban, and rural regions, consisting of 55 families and 15 individuals engaged in waste management activity. The youth leaders were eager to make changes, being frustrated at the authority’s inability to solve local pollution. The collaborative teams were able to continue the work through the time period using various technology platforms. The technology leadership of youth enabled a trusted connection among youth and community members. The research work shows promise to increase communication, collaboration, and collective engagement in local problem solving. However, engagement is not gender neutral, and the work presented reflects societal gender-based challenges which requires attention. This work is expected to provide opportunities for low-resource communities towards problem solving using existing technology platforms.

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)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.254
Teacher spread0.203 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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