Basic Income Simulations for the Province of British Columbia
Bibliographic record
Abstract
An important component of the work to be completed by the British Columbia’s Expert Panel on Basic Income is to design simulations to look at how various basic income (BI) models could work in B.C. (B.C. Poverty Reduction, 2018). The intent of these simulations is to identify the potential impacts and financial implications for B.C. residents of different variants of a BI. Given the poverty reduction targets passed by the B.C. government, detailed in Petit and Tedds (2020d), the potential impacts include those on the incidence and the depths of poverty in the province (B.C. Poverty Reduction, n.d.). The panel ran over 16,000 different BI scenarios to consider in B.C., which were modelled using Statistics Canada’s Social Policy Simulation Database and Model (SPSD/M) program. We evaluate different BI scenarios in terms of their implications for a variety of measures, including cost, number of recipients, rates of poverty, depths of poverty, distributional affects, and inequality impacts. This paper provides details regarding these simulations. Our goal in this paper is simply to consider different versions of a basic income in terms of both their cost implications and their implications for poverty reduction. We believe that identifying the most effective variants of a basic income in terms of these two criteria will help sharpen the conversation about the applicability of a basic income as a policy option for B.C.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".