BREAKING SELF-MISCONCEPTION DRIVEN EMOTIONAL LOOPS OF MBA STUDENTS TO HELP THEM BECOME RESPONSIBLE LEADERS
Bibliographic record
Abstract
Responsible leadership training requires development of individuals who are both knowledgeable and emotionally mature so that they can overcome personal biases to make honest and ethical decisions that have a positive social impact within and outside the organization. Current MBA class exercises use a few trait-based surveys and basic techniques to manage one’s emotions along with leadership definitions that can be misinterpreted by students to be devoid of liable behaviors. Consequently, the problem of self-misconception persists with no change in students’ reasoning about the core problem that is causing their emotionally charged decision. Hence, most students fail to sustain their emotional management processes. To address this need to recognize and correct one’s self-misconceptions to uphold emotional maturity, our specific course of action is to address it holistically based on a preexisting Upanishadic model. The primary contribution of this paper is to bring to the forefront a practical, and useable model that can provide clear steps to refine one’s habitual orientations caused by self-misconceptions. We present the causal mechanism underlying the cognitive-emotional mechanisms wherein the core constructs are Knowing, Active and Inert qualities along six behavior influencing areas which elicit three distinct groups of emotions resulting in consequent decisions. Using a short case scenario-based exercise, we put forth steps students can take to develop responsible leadership qualities. Implications in the form of less stressful and happier workplaces are briefly discussed. A new definition of leadership is presented that helps one distinguish true leadership from notorious ones. The model and the accompanying steps help MBA students develop into fair, thoughtful, knowledgeable, compassionate, and truthful leaders, who work for the benefit of the entire society.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".