Bibliographic record
Abstract
Report on Social Security for Canada, written in wartime, presented to Canadians a picture of a better life in the postwar world. It outlined what governments could do to ensure that all citizens could afford the food, clothing, and shelter necessary to participate fully in their community. Authored by Leonard Marsh for the wartime Federal Advisory Committee on Reconstruction, the report was the subject of enormous attention when it was presented to the House of Commons in March 1943. Drawing on the work of his mentor, William Beveridge, and of John Maynard Keynes, Marsh primarily recommended an employment program meant to ensure lower unemployment and higher incomes. His report also discussed family allowances to make certain that no child would go without, health care insurance, temporary assistance in case of illness, a pension plan, and various other social benefits related to maternity, disability, loss of employment, and death. Today Report on Social Security for Canada is seen as a foundational text for the Canadian social security system. In this edition Allan Moscovitch provides the historical context, an outline of Marsh’s accomplishments, and suggestions for how to enhance the welfare state and respond to the social needs of Canadians in the twenty-first century.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.013 |
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".