Bibliographic record
Abstract
The period between 1450 and 1850 in Europe is often referred to as the ‘Little Ice Age’ but it was in the years 1739-1741 that Ireland experienced some of the most severe weather conditions ever recorded in the country. The Great Frost, as it later became known, caused unprecedented disturbance in Ireland’s ecology: lakes and rivers were frozen, potato crops and grain harvests were ruined, and livestock and humans perished from hunger and disease. This devastation of the natural world was accompanied by upheaval in civic life, including the break-up of rural communities and an increase in crime and social unrest. Though the extraordinary weather witnessed at this time has largely been forgotten, it calls attention to the impact of climatic conditions on both human and non-human environments, as well as exploring the challenge these circumstances presented to existing human perceptions of the relationship between man and nature. Many of the poems written and published at the time explore this unprecedented experience, some drawing on the conventions of poetic representations of the natural world, others offering innovative expressions of diverse conditions. Using the work of well known figures such as William Dunkin and Laurence Whyte, as well as hitherto uncollected texts by lesser-known and anonymous writers, this essay will explore the poetic mediation of this important environmental event and consider its impact on our understanding of the natural world in Ireland in this period.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".