Precarious Positions: Policy Options to Mitigate Risks in Non-standard Employment
Bibliographic record
Abstract
With the potential of precarious work to limit consumer willingness to spend, delay family formation and create too much uncertainty in the labour force, governments are paying close attention to these issues in Canada and abroad. Further, they are looking at a number of tools to address these issues, including changes to labour legislation and improvements in safety nets. But how widespread are employment risks and insecurities, and is it getting worse over time? In this Commentary, we look at the common meanings of precarious work in academic and policy research finding that various meanings help bring attention to employment arrangements with elevated insecurity. We examine trends in non-standard work in Canada and find that the overall prevalence of non-standard work has stabilized over the last couple of decades after growing sharply in the early 1990s. Non-standard work tends to be more insecure than “traditional” jobs, so its persistence over time and, in particular, increases in the prevalence of temporary employment – with large concentrations in health, education, and food services sectors, among others – prompts a deeper investigation. Many forces contribute to the creation of non-standard work. They include factors such as business desires for flexibility – often associated with globalization and technological change – but also worker preferences, which play a major role. In our view, the complexity behind causes of non-standard job creation, and the lessons from some international attempts to address specific areas of concerns through blunt legislative tools, militates in favour of looking to options that bolster the safety net. We think that although reviews of labour laws and their enforcement may lead to constructive discussions and new ideas to improve enforcement, interventions to shape employment arrangements with legislation pose the greatest risks of stymying job creation. In this Commentary, we present a list of options to reduce the income-related vulnerabilities and uncertainties faced by many non-standard workers. These include reducing gaps in health coverage, improving Employment Insurance (EI) eligibility, boosting access to social programs, and ensuring uptake of programs that improve access to education and skills training programs for workers. All of these options should help policymakers design the social safety net in ways that mitigates common risks in non-standard work, while supporting labour market dynamism.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".