Environmental Criticism and the Value of Education: A Study on Indonesian Literary Cyber
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".