A Psychometric Evaluation of Skill Clusters and Practices Used by Highly Effective Executive Presenters
Bibliographic record
Abstract
The present paper is an evaluation of those skill clusters and practices that are associated with superior ratings of executive presentation skills. It focuses on improving executive presentation skills. It explains the basics of oral presentation skills in general that apply across domains including that of business and management. Six sets practices were found to be characteristic of effective executive business presentation: the preparation, the delivery, and the questions and answers that follow the delivery. As Pappas and Hendricks (2000) and DiStanza, and Legge, 2002 found that an effective executive presentation includes the presenter's mastery and skill in technical content, organization, delivery and relating to the audience. Effective presenters have must be proficient at collecting, selecting, organizing, and illustrating their data, and have to be acutely aware of the purpose of their presentation, and the needs and interests of the audience. So, what distinguishes an executive presentation from other forms of oral communication is the context (environment), the content and the audience. The ingredients of an effective executive presentation are more or less the same as those of any other types of face-to-face presentations. Hence, although this article is focused on helping executives develop and more effectively use their oral presentation skills and practices, our framework can be of used by others who want to be effective public speakers.
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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".