Enhancing Interpersonal Intelligence for Management Educators
Bibliographic record
Abstract
Management education is not a rare domain anymore. People learn, study and teach management in a diversity of branches of knowledge or disciplines. In this perspective, management educators are required to be people-smart. The ability to administer their inner feelings as well as to work with people will improve the quality and effectiveness of management teaching. To reach this, personal intelligences which include general intelligence (Intelligence Quotient {IQ}), emotional intelligence (Emotional Quotient {EQ}) and interpersonal intelligence (People Quotient {PQ}) are essential. . This paper will tackle the issue of enhancing interpersonal intelligence (PQ), which also takes into account the aspect of emotional intelligence (EQ) among management educators. To ensure success in interpersonal intelligence, several criteria can well predict people’s quotient. These are the ability to understand students, the ability to grasp people/students’ ability to clearly establish thoughts and feelings, ability to ask and offer feedback, ability to serve as a determinant to how others act and think, ability to engage in and resolve conflicts, and, ability to work with others effectively. It is anticipated that management education will not only perform well in the content and structure but in the psychological aspects of management educators as well.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".