Bibliographic record
Abstract
This dissertation explores the nature and incidence of several non-standard work arrangements (NSWAs). Statistics confirm the growing prevalence of NSWAs. By 1995, less than one third of Canadian workers were employed in a single full-time, permanent job with a "normal" work schedule. Conventional wisdom suggests that the net effect of the increasing incidence of NSWAs is negative for workers. However, certain NSWAs potentially provide better work-life balance for employees and more flexible utilization of labour for employers. Thus, it is suggested that far too little attention has been paid to the varying nature of particular NSWAs. A typology of NSWAs, consisting of five dimensions and three types, is conceptualized. After examining the dataset and some preliminary data analysis, a modified typology of four dimensions and two types is presented and analyzed. In particular, the two key types of NSWAs are categorized as employee-friendly or employer-friendly. In addition to the typology, the workplace and worker characteristics that affect the incidence of NSWAs is examined. This dissertation has a quantitative research design, and utilizes Statistics Canada's 1999 Workplace and Employee Survey (WES). The chosen dataset and methodology also allow inferences to be made regarding employer strategies. Results suggest that job satisfaction is positively related to employee-friendly NSWAs but negatively related to employer-friendly NSWAs. When controlling for a range of worker and workplace variables, it was found that industry, occupation, gender, tenure, and employee participation are related to the incidence of NSWAs. Finally, consistent with existing research, only a tenuous link was found between workplace outcomes and the incidence of NSWAs. The implication is that the implementation of NSWAs is affected more by employers' strategic choices rather than economic necessity.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".