Happyland: A History of the âDirty Thirtiesâ in Saskatchewan, 1914-1937
Bibliographic record
Abstract
"Dirty Thirties" is the sobriquet commonly applied to the agricultural crisis in the drylands of southern Saskatchewan in Canada that coincided with the Great Depression, and it is generally assumed that prior to this period healthier, normal conditions prevailed. In Happyland, Curtis McManus contends that the "Dirty Thirties" actually began much earlier and were connected only peripherally to the Depression itself. McManus has mined the rarely consulted records of Rural Municipalities in Saskatchewan, as well as government documents, ministerial correspondence, local community histories, newspapers, and publications of relevant government departments, to tell a story of a quarter-century of stubborn persistence but also of absurdity, despair, social dislocation, moral corrosion, and inconsistent and often inept government policy. Thanks to McManuss rare and welcome blend of sound scholarship and living breathing prose, it is a gripping and evocative story as well.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".