Bibliographic record
Abstract
a n d r e w c oy n e "Why would a life-long Conservative," asks Hugh Segal in his preface, "support a guaranteed income?"His answer, in this engaging book -part autobiography, part apologia -is that there is no contradiction between the two, for which Segal's own life story is perhaps the best evidence.No one reading Segal's affecting account of his upbringing on the "cheery edge of poverty" could come away with the belief that his interest in the issue is anything less than sincere, or personal.No one with any familiarity with his long career in politics and public service could likewise doubt the depth of his commitment to the Conservative and indeed the conservative cause, even as a child of the working poor and grandson of a union activist.And yet there is a note of wistfulness in the telling.For Segal's brand of conservatism -humane, optimistic, as committed to spreading opportunity as to cutting taxes -is seemingly on the way out.And the other great cause of his life, the basic or guaranteed annual income, seems scarcely closer to being realized than when it first seized his imagination as an idealistic nineteen-year-old, fifty years ago.From the 1969 Conservative "thinkers" conference that introduced the future senator to the idea, to the 1971 report of the Special Senate Com mit tee on Poverty, to the Macdonald Royal Commission in the 1980s, to recent pilot projects in Ontario and elsewhere, the concept of a basic income guarantee -a radical simplification and rationalization of existing income support policies into a single unconditional transfer -has not lacked for high-powered enthusiasts.Indeed, it is often noted that it has support across the political spectrum: though often linked with the left, an early and influential proponent was the libertarian economist Milton Friedman.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.797 | 0.763 |
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".