Examining Historical Fluctuations of Basic Income Policy Commitments in Canada
Bibliographic record
Abstract
Recent discussion in Canada on the need for guaranteed or basic income has been lively and widespread. At the political level, Canada has introduced policy commitments for a universal basic income since the 1960s, proposed by a variety of political parties ranging from the Social Credit Party of Canada, NDP, Liberals and Greens. These commitments, however, have yet to form into a fully-fledged basic income program. Why were these basic income proposals and pilot projects introduced, only to then be dismissed from becoming a real social program? The absence of a universal definition of what is meant by the term “basic income” only serves to further complicate policy proposals brought forth by parties. Other terms that appear to be related to basic income, like guaranteed income, livable income, citizens income, participation income, and universal income seem to be used interchangeably. This paper will investigate the evolution of political commitments toward basic income in Canada that have existed since the 1960s through an analysis of the proposed policies and a jurisdictional scan of provincial attitudes throughout Canada. The results of this investigation indicate a cyclical pattern in which political parties can easily begin by forming the necessary courage and ambition needed to jumpstart the idea of a universal basic income program, only to fail in the final stages of implementation. Even if the party succeeds in initializing a test pilot experiment that may last several years, they may still find it difficult to reach the end of goal of implementation due to a variety of overshadowing obstacles such as inadequate funding, a change in the governing party, or delays and poor execution. The jurisdictional scan suggests that focusing efforts to mix existing welfare programs into a singular basic income program may yield more realistic and achievable results, as opposed to trying to create a large separate program from scratch. An analysis of the philosophy and values of each political party and their platforms tells us that the Green Party is most dedicated toward commitment for a basic income; specifically a “needs based” basic income for people such the disabled or youth in foster care, who are the most deserving and eligible.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".