Bibliographic record
Abstract
‘Stratospheric ozone depletion’ or ‘ozone layer depletion’ had been a public concern for more than two decades. During the period, environmental educators made global efforts to foster learners’ knowledge and understanding of this issue, and to change learners’ behavior to act in favor of protecting ozone layer. Different from other articles in environmental education (EE), however, this article has its focus on an environmental issue which has already been resolved mainly based upon international collaboration and institutions such as the Montreal Protocol to phase out ozone depleting substances. For that, individual citizens’ or students’ efforts such as “NOT to use spray cans with CFCs (chlorofluorocarbons) inside” could not be an effective solution, ironically. Reviewing the discourses and practices of EE critically with the ozone layer depletion case, this article conceptually reflects how EE practices with focus on individual citizens’ or students’ behavioral change may not always be appropriate in dealing with some environmental issues. This article will discuss some possible ways to deal with these environmental problems, issues or phenomena in EE. This article also provides topics for discussion in teaching environmental problems, issues or phenomena based on new perspectives on roles of EE.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".