'Inequality is the Root of Social Evil,' or Maybe Not? Two Stories About Inequality and Public Policy
Bibliographic record
Abstract
Income inequality is on the rise, and everyone, from President Obama and Pope Francis to Prince Charles and Standard & Poor's, is talking about it. But these conversations about what are arguably the most significant changes in the distribution of incomes and earnings since the 1940s are leading to very different views on how public policy should respond. This is as true in Canada as it is in almost all of the other rich countries where inequality has risen. In this paper I tell two stories about inequality – one from the perspective of those who feel it is not a problem worth the worry, and the other from the perspective of those who see it as "the defining challenge of our time" – in order to clarify the issues facing Canadians, and what public policy should do about them.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.076 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".