Insights into the Human Face of the Ontario Basic Income Pilot: Fragments of a Shattered Social Experiment in Thunder Bay, Canada
Bibliographic record
Abstract
Abstract In 2018 the Ontario Basic Income Pilot was launched in three Ontario cities and was cancelled abruptly months later after a change in government. This paper summarises the results of a qualitative study in one of those cities, Thunder Bay, Ontario. In partnership with a local community legal clinic, we interviewed 20 former recipients of the program and 13 key informants to understand two things. First, how did people experience the Ontario Basic Income Pilot for the time it was active? Second, how did people experience the cancellation of the Ontario Basic Income Pilot? After analysing the interviews using Thematic Analysis, we provided answers by way of themes. In general, the Ontario Basic Income Pilot was experienced as positive. Recipients discussed six main benefits: (a) improved financial security; (b) improved food security; (c) increase mental and physical health; (d) improved social mobility; (e) increased humanization; and (f) improved social inclusion. The cancellation on the other hand had largely negative effects. This includes a reversal of all of the aforementioned benefits as well as a decreased trust in government. We conclude the paper with a brief discussion covering two points. First, we explore what our small qualitative study suggests about the intended aims of the Ontario Basic Income Pilot; we do this by loosely comparing our findings with the preliminary survey administered on behalf of the provincial government. Second, we consider what a basic income might mean for the unique circumstances in which Thunder Bay finds itself.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.035 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".