Why Did We Choose to Industrialize? Montreal, 1819–1849, by Robert C.H. Sweeny
Bibliographic record
Abstract
This is an ambitious book, by turns illuminating and opaque, stimulating and frustrating, global and parochial. The cover conveys some of this ambiguity—Why Did We Choose to Industrialize? in slightly smaller font than Montreal; and 1819–1849 above a sepia-tinted image of smoking factory chimneys dated ‘about 1890’. There are at least three books trying to make themselves heard through this complex text. One is an academic autobiography. Robert Sweeny refers to his book as a ‘journal’ and tells the story in the order in which he has conducted his research over a period of forty years, setting each stage in the context of a conversation, conference presentation or incident which foregrounds methodological or epistemological debate. He emphasises that how he asked the questions determined the results he obtained. The key to asking better questions was learning how to listen to the sources and not simply to quarry them for information to answer questions based on our values and ways of seeing. Sweeny works with quantitative sources, but he dismisses much quantitative analysis as ahistorical, lacking any grounded understanding of the past. He uses the nominal and quantifiable sources associated with modernity—census documents, city directories, large-scale maps, property tax assessments—not so much as objective descriptions of what existed, but as evidence of changing attitudes towards the people and things that were being recorded: new ways of thinking about real property, gender relations, the family, the demarcation between work and home. He favours an idealist form of history, espousing Collingwood’s objective of rethinking the thoughts of protagonists to establish their agency in processes of change, but this has to be read against Sweeny’s own humanism, radicalism and self-styled marginalisation, and the agenda implied by those values. Nevertheless, he offers a persuasive argument about the use and abuse of historical sources, which could be read with profit by established researchers as well as graduate students.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".