Bibliographic record
Abstract
Since the collapse of the global financial markets in 2008, economists and commentators have looked back to the Great Depression of the 1930s to discover similarities and solutions for recovery. Contributing to this crucial moment, renowned economist A.E. Safarian has added a new preface to his classic study of the Great Depression, discussing the present crisis and suggesting ways in which future crises might be avoided. Essential reading for economists, historians, and politicians, The Canadian Economy in the Great Depression is the definitive study of the country's worst period of economic failure, covering the period from the stock market's rise in the roaring 1920s, through the Great Crash, to the destitution of the 1930s and the eventual economic recovery. Countless students, journalists, and political leaders, including current US Federal Reserve Chairman Ben S. Bernanke, have used it to better comprehend the complicated nature and history of the markets. With remarkable clarity Safarian untangles the web of relations that led to - and sustained - the Great Depression while also examining the economic controls and stimuli put in place during the Depression and how and why these measures failed. This new edition introduces The Canadian Economy in the Great Depression to a new generation, particularly those concerned about the possibility of another Great Depression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".