Health and Coverage At Risk
Bibliographic record
Abstract
Given the similarity of the two countries and their interconnections, one would expect Canadian employers to act much like their American counterparts, especially since firms from both countries often compete in the same markets using similar technologies (Verma and Thompson 1988).The similarities in the two countries and their integration through trade make it likely that the policy experiences of one have relevance for the other (Gunderson, Hyatt, and Pesando 1996).This chapter provides an introduction to some of the conceptual issues concerning compensation risk bearing by workers in labor markets.The following discussion provides background and is more abstract than the other chapters, which discuss the evidence concerning changes in risk bearing in particular aspects of compensation.This chapter provides a framework for thinking about some of the issues raised in the more applied chapters, and it concludes with an overview of the remainder of the book. COMPENSATION RISK BEARING IN LABOR MARKETS Conceptual IssuesRisk is an element of all aspects of employee-employer relationships, including pay rates, working time, and employment security.The allocation of risk bearing determines the extent to which risks are borne by workers, by firms and their stockholders, and by government.Labor market risks may pose serious problems for some workers.Many workers have mortgages and large financial commitments for rearing and educating children.Fixed financial commitments become problems for workers who face decreases in income due to unemployment or decreased work hours, or increases in expenses due to medical bills not covered by health insurance.Employers face risks affecting their demand for labor due to changes in their factor markets, technology, exchange rates, international competition, domestic competition, the legal environment, tax policy, and macroeconomic conditions affecting demand for their product.Other demand-side factors that may affect workers' risk include OUTLINE OF THE BOOK Risks to Wages: The Traditional and Contingent Workforces Chapter 2: Wage and job risk for workers Aspects of job insecurity include the duration and incidence of unemployment, involuntary nonstandard work, and short-tenure jobs.Chapter 2 examines the question, has worker risk concerning hours, wages, and employment increased in traditional employment relationships in the United States and Canada?Some analysts have suggested 3 Risk in Employment Arrangements
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".