Bibliographic record
Abstract
If your CEO has a sudden heart attack, do you know who will take the chief executive's place?What if your top executives are wooed away to another firm?Do you have the next generation of leaders ready to fill those roles?The only way to reduce the effect of lost leadership is through a strong succession planning program that identifies and fosters the next generation of leaders through mentoring, training and stretch assignments, so they are ready to take the helm when the time comes.Research also supports sound succession planning."Every company has a succession planning document," says David Larcker, a professor in the graduate school of business at Stanford University.The question you have to ask is, "Will it be operational?"The Roadmap of this paper offers the importance of succession management, its benefits, process of succession management, succession management and family business issues etc.Jim Skinner, former CEO of McDonald's Corp., was known to tell managers: "Give me the names of two people who could succeed you."It was just one way the CEO continued the culture of succession planning at McDonald's.It was an understandable priority considering Skinner only landed in the role in 2005 after two other CEO's died suddenly over the course of just two years.And when he retired in 2012, Skinner was confident that his successor, Chief Operating Officer Don Thompson, was ready to take over, because he spent much of his seven years mentoring him."Ibasically felt the responsibility to the board of directors to be sure I provided them with someone who could run the company when I'm gone," Skinner told Fortune a year before his retirement."Until I was capable of doing that, I would not have left."Thiskind of leadership level commitment to training and mentoring the next generation is a vital component of succession planning.And while most executives understand the importance of succession planning efforts, few of them believe their organization excels in this category.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".