Deciphering Deservedness: Canadian Employment Insurance Reforms in Historical Perspective
Bibliographic record
Abstract
Abstract In 2012, the Government of Canada introduced reforms to Employment Insurance (EI), Canada's primary income security programme for the unemployed. The changes entailed new requirements for particular types of claimants to apply for and accept jobs of increasingly less pay, and codified the efforts claimants must demonstrate in job searches to maintain benefits. These measures leave many claimants little choice but to accept precarious employment as a means of financial survival. The central claim of the article is that recent EI reforms are not adequately understood as an instance of neo‐liberal activation. Instead, they must be situated in a long history of attempts to categorize the unemployed as deserving or undeserving of income security on the basis of their work history and perceptions of their willingness to work. Through a survey of different periods of unemployment policy in Canada, we demonstrate continuity in authorities' efforts to differentiate the unemployed into categories of worthy and unworthy. Within this history, however, the 2012 reforms are unprecedented in the extent to which they reorient the EI programme to service low wage labour markets. By way of conclusion, we suggest that the current EI programme exacerbates insecurities for the growing segment of workers in precarious employment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".