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Record W4410977581 · doi:10.53103/cjlls.v5i3.210

Environmental Criticism and the Value of Education: A Study on Indonesian Literary Cyber

2025· article· en· W4410977581 on OpenAlexvenueno aff

Bibliographic record

VenueCanadian Journal of Language and Literature Studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianValue (mathematics)CriticismLiterary criticismPsychologyLiteratureComputer scienceArtPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This research is an effort to describe and explain the values of literary ecocriticism and education in poetry contained on the Riau Sastra website through a review of literary ecocriticism and educational values.The research method uses hermeneutics.Source: Research data is poetry from the Riau Sastra.comweb.The results of the study show that the poems in Riau Sastra represent environmental pollution in the form of water, air and soil, excessive deforestation, exploitation of natural resources.And in it there are educational values.The poems in Riau Sastra represent natural exploitation, deforestation, water pollution, air pollution, natural disasters, and animal exploitation.In addition, the poems also represent educational values, which include religious, cultural, social and moral.Poems are not only as reading materials, but also as a source of messages, information that will have an impact on understanding the environment and educational value on the importance of protecting the environment.Therefore, it takes a strong effort to understand and provide an understanding of how important the value of criticism is in the environment and education using literary appreciation and one of them is poetry.In addition, efforts are needed to build an understanding of the importance of protecting the environment and aspects in it such as not polluting the environment, whether it is water, air and soil, not overbeating forests, exploitation of natural resources for future interests.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.214

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 head0.003
GPT teacher head0.231
Teacher spread0.228 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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