Bibliographic record
Abstract
Between the 1820s and the Second World War, around 2.3 million Scots emigrated. Pre-nineteenth-century Scottish emigration included, variously, a significant mining community which went off to ply its trade in Poland, pedlars and merchants across the Baltic states, the plantation of Ulster and Catholics and Jacobites exiled in France, Germany, Italy and Spain. From the nineteenth century two principal phenomena accounted for Scottish emigration: the British Empire and the Clearances. One interesting pre-Clearances moment in Scottish literature is provided by Robert Burns in his poem, ‘Address of Beelzebub’ (1786). Here, satirically, Burns’s diabolic narrator salutes the earl of Breadalbane, president of the Highland Society. This had supported landlords such as Mr Macdonald in maintaining his people in abject poverty through preventing their projected emigration from his estates of Glengary to Canada. Burns’s knowledge of diaspora was of largely exciting greater potential abroad. This too, however, was not without a darker side, as Burns seems for a period in 1786 to have contemplated going to Jamaica to work as an official in the slave plantations. This route to greater prosperity was a well-trodden journey for hundreds of Scotsmen of good education but limited prospects at home in the latter half of the eighteenth century. Burns in the end did not go, but his possible alternative life in the West Indies is well imagined in a modern novel by Andrew Lindsay, Illustrious Exile (2006). Tobias Smollett responds to another strong element of pre-nineteenth-century Scottish diasporic experience in his novel Roderick Random (1748), partly dealing with life on board a Royal Navy ship as a surgeon (along with the tobacco and slave trades, the military was the means by which many eighteenth-century Scots were introduced to, and frequently settled in, the New World). Although not without criticism of practices in the British navy, Roderick Random is generally enthusiastic about the imperial enterprise in which Britain is involved. Smollett himself had served on a man-o’-war and he is Scotland’s first major writer to live for a large part of his life abroad, not only in England, but in the West Indies (1741–4) and in France and Italy from 1766, Smollett dying in the latter country in 1771.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".