Bibliographic record
Abstract
The onset of the knowledge era has affected all industries. Without exception, the Canadian financial services industry has transformed itself due to the knowledge-intensive structure it possesses. However, high competition and career-minded professionals have created a situation in which leading financial services firms are losing key human capital each day -- capital that can and will be used against them in the modern, fast-paced labour market. In the fight for the brightest senior executives, portfolio managers and fund administrators, human resource professionals must pay attention to the investments they are making in their employees through training and development, while monitoring reward and recognition programs, so that loss of intellectual capital is kept to a minimum. This study examines 19 Canadian financial service firms and their current human capital practices. Results show that while human resource managers are effectively managing the people in their organizations through training and development, performance reviews, and the effective management of fluctuating workforce demands. However, this study highlights the need for greater attention to be paid to the leveraging of human capital that exists within their knowledge-intensive workforce. Furthermore, research findings strongly suggest the need to increase knowledge management behaviours such as the valuation and codification of organizational knowledge assets.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.866 | 0.731 |
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".