Exploring the Black Box of Managing Total Rewards for Older Professionals in the Canadian Financial Services Sector
Bibliographic record
Abstract
This study extends our knowledge about the management of older employees in the sector of financial services, which faces enormous transformational pressures (e.g., emergence of artificial intelligence, digital services). Based on the black box model of human resource management, we investigate how executives at 16 major financial institutions manage their total rewards to motivate their older professionals to stay at work longer. Top management's views towards older professionals underlie a firm's culture or climate, and more precisely, the extent of the perception that they are a strategic resource that needs focused management. Across firms, such adaptation (or lack thereof) is made through the following total rewards components: (1) flexibility in working time and place of work, (2) hiring of retirees, (3) hiring or promotion of older professionals, (4) role adjustment, (5) responsibilities and performance standards, (6) monetary rewards, benefits, and (7) recognition, succession planning, and support for retirement planning or preparation. The black box model should be used in future research to understand which reward components work best in which contextsto motivate older workers to stay at work longer.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".