Just Notice Reform: Enhanced Statutory Termination Provisions for the 99%
Bibliographic record
Abstract
The current state of affairs in Ontario for average and low wage earners who lose their jobs without cause is not satisfactory. These terminated employees must choose between two unappealing courses: either accept minimal entitlements to notice under the Employment Standards Act, 2000, or seek to obtain what may be a greater entitlement under common law, but which may also require engaging in long and costly litigation. Moreover, there are good reasons to question the reliability and effectiveness of the individualized approach to notice determinations undertaken by courts. The practical inaccessibility and inadequacy of these options has been recognized repeatedly, yet these weaknesses have not been addressed by statutory or common law reform. This article addresses the lack of realizable, “just notice” entitlements for employees. Rather than making a detailed proposal for reform, this article seeks to provide a focused examination of the shortcomings of the existing situation, and identify possible avenues for reform. We first provide an introduction and critique of the current individual termination entitlements at common law and under the ESA, including the purposes of such requirements and the results of empirical research. Next, earlier proposals for reform are reviewed. Finally, we set out a series of considerations for “just notice” reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".