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Record W4313328018 · doi:10.33002/jelp02.03.04

Prospects and Challenges to Prove Environmental Harm in Litigation: Status Quo In Nigeria

2022· article· en· W4313328018 on OpenAlexvenueno aff
Awodezi Henry

Bibliographic record

VenueJournal of Environmental Law & Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsHarmDamagesEnvironmental lawEnvironmental justiceEnforcementStatus quoPremisePolitical scienceLawBusinessLaw and economicsEconomics

Abstract

fetched live from OpenAlex

Environmental litigation and enforcement of environmental rights remain a global challenge to sustainability, especially in developing countries such as Nigeria. The increasing rates of industrial activities have led to increase in production of hazardous substances posing threat to lives of the inhabitants of the environment. Victims of environmental harm most times find it difficult to protect and enforce their environmental rights. Proving environmental harm such as damages to property in litigation to enforce rights of compensation or restoration for damages suffered becomes difficult due to locus standi technicalities and undue delays during trials. Sometimes victims are faced with financial constraint in pursuing the course of justice which involves retaining the services of a lawyer and expert witnesses. This paper, therefore, examines the prospects and challenges to proving environmental harm in litigation. This paper employs doctrinal legal research methodology and content analysis of both primary and secondary sources in relation to proving environmental harm in litigation. On this premise, this paper recommends the application of the principle of Res Ipsa Loquitur in trials of environmental cases. Proving environmental harm for the enforcement of environmental rights by victims, should be totally devoid of technicalities of law during trials. This will in turn promote the course of justice in cases dealing with environmental harm.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.891

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.271
Teacher spread0.257 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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