Will my pension be enough? Bridge employment intentions of Canadian Armed Forces members
Bibliographic record
Abstract
Introduction: Although retirement planning in the general public has been well studied, there is little such research regarding Canadian Armed Forces (CAF) members. Prior research indicates that despite a strong pension plan, CAF Veterans often continue to work upon retiring but experience a drop in earnings compared with their military salaries. Methods: This study used a sub-sample from the Military Member/Family Finances Survey, conducted in spring 2017, including only CAF members with 16 or more years of experience and who would be eligible for their full pensions in the next 5 years or less ( n = 873). Results: It was found that approximately one-third of the CAF members surveyed were not certain of what their pension was worth at the time of the survey or would be worth upon retirement. Furthermore, over half the respondents indicated intentions to work after retirement from the CAF. Significant differences were found by sex and rank regarding both pension planning and intentions to work after retirement: female members and junior non-commissioned members appeared less knowledgeable of their pensions. Discussion: More targeted information sessions may be required for women and members of lower ranks in the CAF to support better post-release outcomes. Of the personnel intending to work after retirement, many showed no interest in earning as much money as they had with the CAF. This indicates that the previously identified drop in income for CAF Veterans may be intentional, as members may prefer less responsibility upon leaving the CAF.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".