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Record W4403605255 · doi:10.18174/179704

The role of households in solid waste management in East African capital cities

2011· dissertation· en· W4403605255 on OpenAlexfundno aff
Aisa Oberlin Solomon

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersWageningen University and ResearchU.S. Environmental Protection AgencyInternational Labour OrganizationJapan International Cooperation AgencyInternational Development Research CentreUnited Nations Development Programme
KeywordsSolid waste managementCapital (architecture)GeographyMunicipal solid wasteBusinessSocial capitalEnvironmental planningEconomic geographyWaste managementEngineeringSociologySocial scienceArchaeology

Abstract

fetched live from OpenAlex

Solid Management is a concern in East African capital cities. The absence of managing solid waste is a serious problem. An ever bigger concern is the growing quantities of waste that are generatedat households level in informal settlements. In most cases proper safeguard measures are largely ineffective or not in place at all. Moreover, unsafe disposal of waste in the region is coupled with poor hygiene. There is no doubt that East African capital cities need to formulate effective ways to manage their waste. This book is a result of PhD research within the framework of the PROVIDE project funded by INREF and carried out in East African capital cities (Dar es Salaam, Nairobi and Kampala). The work was carried out at the Environmental Policy (ENP) group with professor Gert Spaargaren and Dr. Peter Oosterveer as promotor and co-promotor. The focus of the research was to highlight the role of households in the production and management of domestic solid wastes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.543
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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.

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

Citations1
Published2011
Admission routes1
Has abstractyes

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