The role of reasonable notice legislation in organizational downsizing decisions in Canada
Bibliographic record
Abstract
Purpose – The purpose of this article is to explore the impact of reasonable notice legislation on organizational mass lay-off practices in Canada. Design/methodology/approach – Information regarding 1,147 mass lay-off events in Ontario were examined using aggregate level data analysis and ANOVA to develop an understanding of the role of legislation on mass lay-off practices. The data represent all Notice of Mass Termination provided to the Ministry of Labour from 2001 to 2008. Findings – The results suggest that organizations choose to absorb inefficiencies during mass lay-offs to reduce expenses associated with reasonable notice periods. Additionally, the findings suggest that the use of mass lay-offs is polarized, with some organizations executing frequent large lay-offs, whereas others execute infrequent smaller lay-offs. Research limitations/implications – This research provides evidence that labour legislation influences organizational decision-making during time of significant organizational change, using an ad hoc review of past organizational event. Further research is required to establish the theoretic basis (motivation, rationalization and perceptions) for these empirical results. Originality/value – As downsizing becomes a business norm, the role of government and the concept of reasonable notice remain largely unexplored. Challenges with data availability continue to pose a significant barrier to effectively integrating both internal and external factors that influence organization level downsizing decisions. This article is very timely and extends the current discourse, by providing a preliminary exploratory analysis on the role of reasonable notice legislation.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".