Prepared for the University of Lethbridge Faculty Association
Bibliographic record
Abstract
The purpose of this report is to assess the impacts of possible changes to the way Canadians retire. Banning mandatory retirement and phased retirement will be the focus of this paper. The large variation across retirement policies in North America provides ample evidence of the impacts of these changes. Below are some of the important results that the available evidence pointed to: • The age effect of banning mandatory retirement is usually small. The upward age effect is more significant for university faculty compared to the average worker but is still small in absolute terms. • Banning mandatory retirement will likely improve a department’s recruitment ability. The prediction that banning mandatory retirement will hurt the opportunities of young academics has not materialized in jurisdictions that have done so. • More productive professors choose to extend their careers past normal retirement age, while less productive professors rarely do so. • There is much debate on whether or not mandatory retirement is discrimination. Some believe it should remain a freely negotiated contractual agreement, while other point to inadequacies in provincial human rights codes which allow for age discrimination. • The age effect of a phased retirement plan is not large. Evidence suggests that those who choose phased retirement would have most likely continued to teach full-time without a phased retirement option. • The goal of phased retirement to keeping highly productive senior staff in the workforce longer is not achieved, though it does accelerate the rate which lower productivity academics leave the university setting. • From the empirical and theoretical evidence available, the benefits of both banning mandatory retirement and implementing phased retirement significantly outweigh the drawbacks. 1
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.685 | 0.415 |
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".