Emotional Intelligence in Undergraduate Accounting Students: Preliminary Assessment
Bibliographic record
Abstract
There is a growing recognition among researchers and practitioners that the workplace is not the strictly cognitively-managed environment it was once assumed to be. Emotions play a large role in organizational life, and emotional intelligence (the ability to recognize, use and manage emotions) has become a skill that may allow accountants to perform better in a variety of areas such as leadership, client relations, and perhaps even decision-making. In addition, it is a skill that employers seem to value and that may be important to personal development as well. Thus, accounting education must attempt to inculcate emotional intelligence in its graduates in addition to technical knowledge. This paper investigates the level of emotional intelligence of accounting students using the MSCEIT, an instrument that measures ability rather than acquired competencies. The results show that the level of emotional intelligence of the students in the sample could be a concern. There is no evidence that one term of traditional accounting education can be expected to provide an opportunity for improvement. Thus, attempts to increase the emotional intelligence of the students may require targeted educational interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".