Labour Market Policies in Denmark and Canada: Could Flexicurity be an Answer for Canadian Workers?
Bibliographic record
Abstract
Abstract The labour market in Canada is changing. Over the past decades there has been an increase in the number of precarious workers on short-term, part-time, contracts; jobs are created and lost, as employers deem necessary. As a result of these shifts in the organization of work, many workers are now forced to hold multiple jobs in order to make ends meet. This move away from long-term employment has created a situation where the majority of Canadian workers can no longer expect their employer to provide predictable support and security for them. At the same time, under the current Employment Insurance (EI) laws, they cannot expect support from the federal government either. How can workers gain some immediate protection through expanded social welfare programmes? With more and more workers, especially women, racialized workers and lower income people relegated to precarious employment, we must question current social policy. If, as it appears, EI does not work, we must strive to implement a viable alternative. Could an alternative system be modeled on the flexicurity system now in effect in Denmark? This paper draws on Nancy Fraser’s criteria for social justice for the globalized worker, to assess the ways that flexicurity could improve the security of the Canadian worker by offering alternatives to participation in the market nexus.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".