Extreme weather, climate variability, and childhood: A historical analogue from the Orkney Islands (1903–1919)
Bibliographic record
Abstract
Many small island communities are said to possess high levels of autonomous coping capacity, often linked to peripherality. This social resilience is dynamic rather than static, with environmental, social, and political drivers shaping local pattens of vulnerability, necessitating reflection on how choices in one area may potentially lead to new vulnerabilities or transfers of vulnerability to already sensitive groups, such as children. This article argues that a historical perspective can help shed light on these dynamics. Impacts of extreme weather and climate variability, and resultant impacts of community coping strategies, on children in early-20th-century Orkney are explored using school logbooks. It finds that extreme weather ‘shocks’ directly impact children’s ability to attend school, while adjustments to the school calendar for agricultural operations constitute an indirect impact of climate variability, with reduced recreation time an emergent effect. Contextualised amidst contemporary island scholarship, two key messages emerge. Firstly, that the mobility and/or work of children in island communities remain sensitive to climate stressors in the present day and, secondly, that the island context itself matters, as characteristics commonly associated with ‘islandness’ — such as smallness, remoteness, and high social capital — may intersect in ways that fundamentally impact children’s experiences of weather, work, and education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".