How Does a Coastal Private-managed Public School Set Sail in the Wave of Transformative Education?
Bibliographic record
Abstract
Transformative education is mandatory for achieving the biodiversity plan. As the section K of the Kunming Montréal Global Biodiversity Framework (KMGBF) states, to effectively implement the framework and achieve behavior change, “transformative education” will require the integration of biodiversity into formal, non-formal and informal education program and curriculum. There are considerable possibilities to transition into transformative education, we need to envision the future, inspect current education systems, and think about what needs to be changed, to be kept and established in order to achieve the transformative education we want. To envision the future for environmental education (EE), the author uses Yue Ming Elementary School which has practiced the Ocean Sustainability Environmental Education in Yilan County, Taiwan as an example to review what approach it takes under the shifting EE, and its interactive relationship with various factors including legislation, changes of parents’ attitude towards education and communities in the social-ecological context. This contribution does not intend to set up a benchmark and tell readers what transformative education should look like. Instead, this work focuses on the interrelationships among diverse factors and provide readers merely one example and one possibility of how EE may and could look like on the pathway to achieve global biodiversity goals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".