Legacies of childhood learning for climate change adaptation
Bibliographic record
Abstract
Using archaeological, historical, and ethnographic analysis of Norse and Inuit toys and miniatures, this paper argues that legacies of childhood learning can create limits to climatic change adaptation and provide lessons from the past relevant today. In Medieval Greenland, Norse children played with objects that would have familiarised them with the expected norms and behaviours of farming, household activities, sailing and conflict, but not with hunting, which was a key omission given the fundamental importance of wild resources to successful climatic adaptation in Greenland after the climate shocks of the mid-13th century. The restricted range of toys combined with an instructional form of learning suggests a high degree of path dependence that limited adaptation to climatic change, and we know the Norse settlement ended with the conjunctures of the 15th century that included climatic change. Inuit children, by contrast, learnt highly adapted behaviours and technologies through objects that taught locally tuned hunting skills. Inuit approaches that prioritised unstructured learning time aided the development of creative skills and problem-solving capabilities, and the Inuit successfully navigated the climatic changes of the Little Ice Age in Greenland. This insight from the past has implications for our approaches to childhood learning in the 21st century and the unfolding climate crisis. Innovative approaches to childhood teaching and learning in the context of climate change adaptation could provide effective solutions, on a timescale commensurate with that of projected climate impacts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".