“We need to take care of each other, and that's what this program was helping to do:” Disabled women’s experiences of the Ontario Basic Income Pilot
Bibliographic record
Abstract
From 2018 to 2019, some 4,000 Ontario residents received money as part of the Ontario Basic Income Pilot (OBIP), a basic income experiment that ran in three municipalities across the province. Drawing on interviews with 15 disabled women who participated in the pilot, this paper mobilizes women’s stories about their lives before, during, and after basic income to critically explore income security policy through a feminist disability lens. Women’s experiences of OBIP reveal important insights about the complex relationship between poverty, debility, impairment, and disability, as well as how targeted income support programs sustain or challenge ableism. This study’s findings suggest that while basic income offered material benefits to disabled women that supported them to survive in an ableist world, it is not inherently immune to the challenges characterizing other income security programs (e.g., the Ontario Disability Support Program, or ODSP). Despite this, women’s stories offer a glimpse at how by offering a more adequate and less conditional income to participants, OBIP created space to practice resistance and imagine different disability futures.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.024 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".