How can Corporations Contribute to the Community through Environmental Education? : Focusing on Partnerships between Corporations and Universities in Korea
Bibliographic record
Abstract
The goal of this study was twofold: first, to determine the effectiveness of providing environmental education in collaboration between a company and a university, and, second, to describe perspective that the environmental education community has developed through close communication with stakeholders in environmental education. For the study, we observed the "Green Keeper" project’s collaborative progress and collected various quantitative and qualitative data on the project for three years. Participating students’ knowledge of and concern for the environment significantly increased after participation in the project (p<.01). We discerned that changes in the children spread to the community as well. Green Keeper project participant satisfaction ranged from 3.52-4.32 over three years, indicating a consistently high level. Satisfaction rose each year because the project steadily evolved through collaboration between the university and the company with additional feedback from the community. This project found several advantages to collaborative environmental education, such as effective community environmental education, encouraging environmental education in schools, and investment in the future of the company.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".