Bibliographic record
Abstract
Educating the Neglected Majority is Richard Jarrell’s pioneering survey of the attempt to develop and diffuse agricultural and technical education in nineteenth-century Canada’s most populous regions. It explores the efforts and achievements of educators, legislators, and manufacturers as they responded to the rapid changes resulting from the Industrial Revolution. Identifying the resources that the state, philanthropic organizations, private schools, moral reform societies, and churches harnessed to implement technical education for the rural and industrial working classes, Jarrell illuminates the formal and informal learning networks of Upper Canada/Ontario and Lower Canada/Quebec at this time. As these colonial societies moved towards mechanization, industrialization, and nationhood, their educational leaders looked to US and British developments in pedagogy and technology to create academic journals, evening classes, libraries, mechanics’ institutes, museums, specialist societies, and women’s institutes. Supervising these varied activities were legislatures and provincial boards, where key figures such as E.-A. Barnard, J.-B. Meilleur, and Egerton Ryerson played dominant roles. Portraying the powerful hopes and sometimes unrealistic dreams that motivated energetic and determined reformers, Educating the Neglected Majority presents Ontario and Quebec’s response to the powerful industrial and demographic forces that were reshaping the North Atlantic world.
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 | 0.004 | 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 | 0.032 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".