Bibliographic record
Abstract
INCREASE LEADERSHIP SPEEDM uch change has occurred in the past few decades in virtually every area of our lives.One of those changes is the dramatic increase of speed in daily business activity.Speed in business is partly a reflection of the overall increase in speed in every area of life.Shopping is faster, delivery is faster, meals are faster, and accessing information is faster .• Technology is a major enabler of speed within business.The Internet, smartphones, and personal computers enable access to information-a process that used to take a week-to occur in seconds.• Competition now comes from all over the world, with every firm seeking to be first to market.Why?Because there is a "first to market" advantage that nearly always results in a dominant share of the market.There is a saying: "the second company to market is the first loser."Staying on top demands speed.Cell phones were invented in the United States, then Nokia (Finland) and RIM/BlackBerry (Canada) took a dominant lead, only to be passed up by Apple and Samsung.It was all about speed in developing and marketing a more fully featured phone.• Finally, being fast is fun.For the individual, not only do you get much more done, you enjoy a greater variety of work and activity.You get the plum assignments.The authors of this article are co-founders of a firm that provides leadership development programs to some of the largest and most successful organizations in the world.One of the tools we advocate is a 360-degree feedback instrument consisting of forty-nine items that describe how leaders behave on a variety of topics.These subjects include the leader's qualities, such as character, initiative, innovation, problem-solving skills, passion for producing results, interpersonal skills, and strategic vision.These skills and
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".