Bibliographic record
Abstract
This study examines the history of Newcourt Credit Group, particularly its employee share ownership programs. Newcourt was a significant Canadian leasing corporation, particularly during the years from 1994 to 1999, when it grew from 143 employees to over 6,000 dispersed in more than 25 countries. The heart of the firm's human resources and compensation program was founder and Chief Executive Officer Steven Hudson's “wealth creation” programs, wherein all staff might share in the financial successes of the firm, and to some degree be exposed to the vagaries of the stock market. Hudson wanted to accomplish this goal through a number of employee share ownership programs aimed at four different levels in the organization: the three founders, the executive group, the managers and the remainder of the employee population. The overall goal of this research was to understand how different levels of the organization relate to the ownership plans introduced by the firm. Specifically, it explored how different perceptions are coloured by their stake in the company, the performance of that stake, and their degree of participation in management. In addition, it examined the role of communications as related to the employee share ownership programs. The research was conducted through a series of focus groups and individual interviews with the four different population groups. The study reveals the value of these programs to some publicly owned corporations. It shows the necessity of explaining to all employees how the various share ownership programs work, and in particular, the risks associated with these financial instruments. The dissertation illustrates that different programs appeal to unique segments of the employee population, and that some groups are more risk averse than others either through choice or necessity. The work also concludes that the share ownership plans must be amended from time to time to meet the changes in the organization as it grows and matures. It confirms the necessity of having all levels share in decisions that may impact them, by providing input and/or their reactions. Finally, the work shows the importance of continuous communications relative to the plans themselves, the health of the corporation and its mission, vision and values. Key to this success is the chief executive officer and the executive team who lead by example.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".