Bibliographic record
Abstract
This essay evaluates the state of the debate around basic income, a controversial and much-discussed policy proposal. I explore its contested meaning and consider its potential impact. I provide a summary of the randomized guaranteed income experiments from the 1970s, emphasizing how experimental methods using scattered sets of isolated participants cannot capture the crucial social factors that help to explain changes in people’s patterns of work. In contrast, I examine a community experiment from the same period, where all residents of the town of Dauphin, Manitoba, were eligible for basic income payments. This “macro-experiment” sheds light on the community-level realities of basic income. I describe evidence showing that wages offered by Dauphin businesses increased. Additionally, labor market participation fell. By ignoring the social interactions that characterize real-world community contexts, randomized studies underestimate the decline in labor market participation and its impact on employers. These findings depend to a great extent on the details of the policy design, and as such I conclude that the oft-proposed right–left ideological alliance on basic income is unlikely to survive the move from basic income as a broad policy umbrella to basic income as a concrete policy option.
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 | 0.501 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".