Employee-Friendly and Employer-Friendly Non-Standard Work Schedules and Locations
Bibliographic record
Abstract
While many studies suggest that non-standard work schedules and locations have negative implications for workers, some indicate positive impacts. The purpose of this paper is to examine the relationship of these non-standard work arrangements (NSWAs) with job satisfaction, after categorizing the former as being employee-friendly or employer-friendly. This supply-side analysis of the labour market has a quantitative research design, and utilizes Statistics Canada's 2003 Workplace and Employee Survey (WES) data. As hypothesized, the incidence of employee-friendly non-standard work schedules and locations is significantly and positively related to job satisfaction while incidence of employer-friendly examples is significantly and negatively related to job satisfaction. In today's business climate, employers have the strategic choice to utilize NSWAs to address their operational needs, or the needs of their workers. Although either might make strategic sense, implementing employee-friendly NSWAs potentially benefits both parties concurrently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".