Testing Time: Uncovering Potential Impacts of Project Duration in Basic Income Pilots
Bibliographic record
Abstract
In recent years, basic income – sometimes referred to as universal basic income, or, guaranteed annual income – has resurfaced as a mainstream policy proposal. Basic income, in its simplest form, is an unconditional cash transfer from government to individuals or families that provides more dignity to recipients when compared to existing social assistance programs. \n \nThere is a growing appetite in Canada to develop more effective poverty reduction strategies, and Ontario has recently taken the lead with a newly deployed Basic Income Pilot Project. This pilot, and others alike, are testing how recipients will use basic income, and whether such a policy would be an innovative replacement for the complicated, contentious, and costly systems currently in place. \n \nThe research question in this Major Research Project (MRP) investigates the potential behavioural differences between short-term basic income pilot projects, and permanent policies. With a permanent basic income yet to be implemented, an experimental method was developed to better understand these potential differences. \n \nUsing ‘Structured Scenario Interviews’, the research found significant differences in the ways participants allocated basic income across two hypothetical time-based scenarios: a one-year basic income pilot; and a permanent policy. This method can be used as a complementary tool to adjust policies in existing pilot projects, allowing research teams to better understand expected behaviours under shorter time horizons. The method is applicable to basic income pilot projects in any jurisdiction.
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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 | 0.077 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".