“Making” Waves: How Young Learners Connect to Their Natural World through Third Space
Bibliographic record
Abstract
In a world that grows increasingly aware of ecological problems such as global warming, rising sea levels, and pollution, we need to reconsider how we connect ourselves to the natural world around us. In this paper, we view makerspaces as ideal locations to shape children’s emotional, sociocultural, and educational consciousnesses about the environment and our multi-layered roles undertaken to live in, and conserve, it. We apply third space, makerspace, and relational value theories in the analysis of a research project conducted with children at an early childhood centre. This project invited children to discuss ocean conservation prompted by the picturebook Flotsam (2006) and create three-dimensional exhibits that express how they visualize ocean conservation. Our research shows that children develop strong emotional connections to tangible representations of conservation when they are given the time to invest in making them, and that these emotional connections are driving forces for relational values that create conservation-oriented mindsets. It also shows how important context is for shaping the ways children learn, and that providing opportunities to examine conservation through makerspaces as a third space encourages children to create empathetic and personal relationships with the natural world.
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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 | 0.002 | 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 | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".