Retirement pension poverty among injured workers with long-term workers’ compensation claims
Bibliographic record
Abstract
Abstract Achieving adequate incomes to sustain people in their retirement years is a challenge. This paper addresses poverty among injured workers of retirement age when they have been receiving workers’ compensation benefits. Guided by governmental management theory, we address procedures that have operated without media or scholarly attention, in order to highlight details in workers’ compensation retirement pension policy that contribute to injured worker poverty in older age in Ontario, Canada. We used a mixed methods approach, applying critical discourse analysis to explain (a) policies related to retirement income; (b) rationales that supported a legislative change that reduced retirement pensions; and (c) workers’ experiences of living with a workers’ compensation pension. Although workers’ compensation board retirement income policy was intended to make up for loss of contributions to the Canadian federal pension fund, the Ontario injured workers’ retirement pension now pays less than half of the amount workers would have received in the federal pension,a trend that is observable across Canadian provincial workers’ compensation boards. Legislative debates about the 1998 bill that halved the Ontario injured workers’ retirement pension centred on neoliberal logic of fiscal responsibility. Injured workers and key informants in this study expressed trepidation about injured workers’ financial futures.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".