MétaCan
Menu
Back to cohort
Record W1999891413 · doi:10.5539/jms.v2n1p217

Environmental Degradation and the Lingering Threat of Refuse and Pollution in Lagos State

2012· article· en· W1999891413 on OpenAlexvenueno aff
Ola Aluko

Bibliographic record

VenueJournal of Management and Sustainability · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodMetropolitan areaEnvironmental planningGovernment (linguistics)Human settlementLocal governmentBusinessAgency (philosophy)Environmental degradationMunicipal solid wasteLocal government areaGeographyEnvironmental resource managementEngineeringWaste managementEnvironmental scienceAgriculture

Abstract

fetched live from OpenAlex

The rapid environmental degradation taking place in Nigeria is increasingly becoming a major threat and is gradually changing the landscape, destroying the sources of livelihood. That is why the problems of pollution and waste management are getting more serious and complex in towns and cities than in villages, and more in densely populated areas than in regions of sparsely settlements. The sudden explosion of refuse dumps in most parts of Lagos led to the creation of Lagos State Waste Management Authority (LAWMA). With the management outfit, the situation is nearing an alarming state. In fact, the metropolitan city is under serious threat of being submerged in rapidly waste and filth. The questions are what is really wrong and where lies the solution amongst the various environmental policies? These questions and many others issues are what this paper were concerned with using empirical data from the metropolitan Lagos.The two major sources of data collection which include primary and secondary data were utilized. The household questionnaire survey was 200 that were randomly administered in the Local Governments of Oshodi-Isolo and Mushin as case studies. The justification of the two selected local governments is based on the fact that they are heavily populated residential areas with heavy wastes generated. The data were analyzed using simple descriptive statistics such as frequencies and cross-tabulation. The results revealed that the government agency and the private operators responsible for solid waste management are both proven inadequate to cope with the volume of wastes generated within the city.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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 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

Citations8
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Management and SustainabilitySame topicUrban and Rural Development ChallengesFrench-language works237,207