Robert C. H. Sweeny.<i>Why Did We Choose to Industrialize? Montreal, 1819–1849</i>.
Bibliographic record
Abstract
In this volume, Robert C. H. Sweeny relates his journey of forty years as a historian of Montreal during its precocious transition from colonial outpost to an industrial hub, a development the author contends that made the city an exemplar. While its temporal and spatial lens is restricted, Why Did We Choose to Industrialize? Montreal, 1819–1849 covers a range of topics from specific issues in the history of Lower Canada, like the roles of merchant capital and bank credit, social differentiation within the peasantry, the constitutional crises of mid-century, and the concentration and segregation of urban space, to larger themes, such as the limits of environmental history, the tension between agency and constraint, the triumph of the liberal order, and the gendering of social and political spheres and property relations. Although the book is difficult to summarize, it ought to find a large audience. If there is a common thread, it lies in the compelling case the author makes for situating source material within the broader context and for critically engaging with these sources in a self-reflective manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".