From seamster to shopkeeper : examining occupational mobility in Shelburne, Nova Scotia, 1783-1795
Bibliographic record
Abstract
This thesis examines the occupational structure in Shelburne, Nova Scotia, from its settlement in \n1783 until 1795. In 1783, following the conclusion of the American Revolution, upwards of 10,000 \nLoyalist from the United States came to the nascent town of Shelburne. In less than a decade, the \nmajority of those individuals had departed, and the population plummeted. Through the statistical \nevaluation of the early settlers’ occupational data, patterns of growing and shrinking markets in \nresponse to historic events will be shown. Shortly after settlement, the new residents in Shelburne \nestablished large manufacturing and market/service sectors but failed to initiate a strong \nagricultural foundation. It wasn’t until the termination of Crown provided food supply that the \nsettlers implemented a flourishing agricultural sector. This research proves that the occupational \nstructure of the town shifted to accommodate circumstance and demand amidst a recovering \nwartime economy. Secondly, this thesis undertook longitudinal studies of the individuals who \ncomprised those sectors to demonstrated that those who were able to adapt their occupations to \nsuit the needs of the town succeeded in long-term settlement, whereas those who were steadfast in \ntheir trades were left no choice but to seek prospects elsewhere. These findings are significant in \nunderstanding the importance of flexibility in the face of economic hardship, particularly in the \ncontext of an eighteenth-century maritime environment.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".