Bibliographic record
Abstract
Recognizing the need to educate young students about climate change, there is ongoing debate regarding the appropriate age and pedagogical approaches for its introduction. Scholars differ in their views on whether to postpone climate change education until higher grade levels due to concerns about children’s cognitive and emotional readiness or to advocate for earlier involvement as a means of fostering civic engagement. To contribute on this discussion, this small-scale case study engaged 7 Grade 3-4 students to explore their perspectives and understandings about climate change. Over a two-month period, these students actively engaged in five one-hour sessions focused on climate-related topics, including weather, climate, and greenhouse effects. Group conversations and drawing activities were employed to foster an environment where the children could freely express their perspectives and experiences. The collected data included both students’ drawings and video recordings capturing session activities and group interactions. The children in this study demonstrated critical awareness and concerns about climate change. They also expressed diverse conceptual understandings spanning from misconceptions and evolving ideas to sophisticated insights rooted in their experiences. Based on the findings, efforts are made to comprehend whether and how discussions about climate change can be initiated with Grade 3-4 students. The research concludes by highlighting the need for more comprehensive studies to investigate age-appropriate K-6 approaches and curriculum that address both the cognitive and emotional aspects of climate change education.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".