Bibliographic record
Abstract
After the Second World War, progressives and traditionalists waged a quieter battle over schools. In Between Education and Catastrophe, George Buri connects the educational debates of the 1950s to the broader Canadian postwar political conversation about the social welfare state and Keynesian versus laissez-faire models of liberalism. Working skilfully with primary sources, contemporary publications, and a rich array of secondary sources, Buri examines debates over curricula, the purpose of high school, teacher training, rural schools, and standardized testing in Manitoba. The progressives who advocated for a "new liberalism" - characterized by government intervention and the social welfare state - sought to create a system of public schooling that would both equip students to succeed and enlarge their political vision by encouraging compromise and democratic decision making. They promoted more practical subjects, child-centred classrooms, and the use of psychological expertise to promote "life adjustment." Meanwhile, self-styled traditionalists such as Hilda Neatby thought progressive education undermined the individual competition and achievement at the root of a laissez-faire economy, calling for a return to the basics, an elimination of "frill" subjects, and a more academic focus for the public education system. A frank consideration of conflict, power, and influence within school systems, Between Education and Catastrophe brings to light compelling social, cultural, and philosophical themes within the history of education in Manitoba.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".