Cultivating Eco-Literate Writers: Exploring the Intersection of Environmental Awareness and Text-Based Writing Skills
Bibliographic record
Abstract
Expanding information related to environmental degradation is one potential approach to enhance students' environmental literacy and awareness (eco-literate). This research investigates the relationship between students' skills in writing popular text-based articles and their ecological literacy. A quasi-experimental research design was employed with a one-group test. The sample consisted of 23 Indonesian Language and Literature Education Program students, selected through purposive sampling. Data collected included scores on writing assignments of popular articles and questionnaire results regarding students' knowledge of ecological literacy. Data analysis techniques involved testing for correlation, regression, normality, multicollinearity, heteroskedasticity, F-test, and t-test. Pearson correlation results showed a significant relationship between students' eco-literate knowledge and their ability to write popular articles, with a coefficient of determination of 55.4%, indicating that 55.4% of the variation in the ability to write articles can be explained by eco-literate knowledge. The regression analysis revealed a strong correlation coefficient of 0.942, indicating a very strong relationship between various aspects of eco-literate knowledge and students' ability to write popular articles. The t-test further demonstrated that environmental concern is the factor that has the most significant influence on students' writing ability, with a regression coefficient (b2) of 1.793.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".