A Narrative Analysis of Basic Income Pilots from an Ontarian Perspective
Bibliographic record
Abstract
The following is a research analysis paper on the Ontario basic income pilot and basic income projects more generally. This analysis will be conducted using a narrative lens. When policies are created or cancelled there is often a narrative that plays a significant role in validating the process. In this paper, the narratives that accompanied the creation and cancellation of Ontario’s basic income pilot— respectively referred to as the ‘creation’ and ‘cancellation narratives’—are unpacked. The creation narrative states that basic income projects will reduce poverty, while the cancellation narrative states that basic income projects unjustly redistribute wealth through raising taxes. These narratives are also present in other basic income pilots; two of these are also analyzed in this paper, namely Finland and Manitoba’s basic income pilots. The paper concludes with a recommendation for policymakers who are advancing basic income projects in the future. Policymakers should advance a metanarrative that bridges the creation and cancellation narratives. This metanarrative would frame basic income projects as reducing poverty without unjustly redistributing wealth. Such a narrative can be used to advance basic income projects that are more resilient than projects analysed in this paper.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".