The need for a federal Basic Income feature within any coherent post-COVID-19 economic recovery plan
Bibliographic record
Abstract
COVID-19 has shone a harsh light on the extent of poverty in Canada. When normal economic activity was interrupted by the exigencies of public health driven lockdowns, the shutdown disproportionately affected people who, before the pandemic, were living on incomes beneath the poverty line or dependent upon low-paying hourly remunerated jobs, usually part time and without appropriate benefits. Those living beneath the poverty line in Canada, three million of welfare poor and working poor, include a disproportionately large population of Black and Indigenous people and people of colour. This paper addresses the challenge of inclusive economic recovery. In particular, we propose that the federal government introduce a Basic Income guarantee for all residents of Canada as part of a comprehensive social safety net that includes access to housing, child care, mental and physical health care, disability supports, education, and the many other public services essential to life in a high-income country. Residents with no other income would receive the full benefit that would be sufficient to ensure that no one lives in poverty, while those with low incomes would receive a reduced amount.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".