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Record W4251930362 · doi:10.32920/ryerson.14646564

Environmental justice and project development : the Sri Lankan experience

2021· preprint· en· W4251930362 on OpenAlexaff
Liana Anoushka Pullenayegem

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInjusticeVariety (cybernetics)Environmental justiceSri lankaEnvironmental planningEnvironmental resource managementEconomic JusticeValue (mathematics)Environmental impact assessmentPolitical scienceGeographyComputer scienceEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

Despite large contributions from academia, there is a significant lack of indicators against which to measure environmental injustice, particularly with regard to project development in developing countries such as Sri Lanka. Indicators and methodological approaches that have been developed and are being used in the West are mostly irrelevant since the types of environmental injustices experienced in the two regions are different. This study presents an "environmental justice matrix", a tool consisting of selected indicators that represent a variety of issues that have the potential to cause environmental injustice and that are encountered during the different phases of project development in Sri Lanka. The matrix is designed to evaluate the degree of environmental injustice that may arise during project development and should serve to keep environmental justice front and centre of every stage of the project, especially during the assessment and decision making processes. The value of this tool is illustrated by assessing two large infrastructure projects against the matrix.

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: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.957

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.051
GPT teacher head0.341
Teacher spread0.289 · 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
Published2021
Admission routes1
Has abstractyes

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