The Targeted Wage Subsidy: How Program Design Creates Incentives for “Creaming”
Bibliographic record
Abstract
Across most developed nations, including Canada, parallel systems of social welfare and employment insurance have increasingly been replaced by programs that emphasize work as a means to achieve welfare goals within the so-called re-employment framework. Various authors have drawn attention to the tension between the goal of long-term sustainable employment, and re-employment-based strategies that emphasize short-term and stand-alone interventions. In this paper, we focus on the implementation of one such program in Canada, the Targeted Wage Subsidy. This program seeks to place the most marginal qualifying participants in employment by offering employers a financial inducement. By paying close attention to the experiences of those tasked with monitoring and implementing the program in Toronto, we identify various ways in which program design elements may systematically disadvantage the intended recipients. These program delivery mechanisms are shaped both in the practices of implementing agents, as well as by the public accountability framework that enforces rigid timelines and reporting requirements, resulting in a practice commonly referred to by employment service providers as “creaming.” Our observations lead us to question whether the target population is, in fact, the one benefiting from these return-to-work supports.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".