Diversity Of Personality Types For Engineering In The New Millennium
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2630 Diversity of Personality Types for Engineering in the New Millennium. Peter Rosati and Judit E. Puskas. The University of Western Ontario Abstract A ten-year study at the University of Western Ontario (UWO) has related the personality type (MBTI) of a large group (n=1865) of Canadian engineering students to the progress of those students through the engineering program to graduation. It has been found that the entry class, in comparison with general first-year students, contains a large proportion of I_TJ (introverted, thinking, judging) types and an even larger proportion of these same I_TJ types graduate in engineering. On the other hand the 'opposite' E_FP (extraverted, feeling, perceptive) types form a much smaller proportion of the entry class and furthermore feature significantly in the group of students who withdraw after or during first year and also in the group who transfer out of engineering and graduate in non-engineering disciplines. Can the engineering profession afford to be under-represented in E_FP types who by nature adapt well to change, enjoy variety and action, are interested in people and are good communicators? As a follow-up to the main UWO study, it was decided to survey a subset of engineering graduate alumni (from the original MBTI cohort) asking about their current job description and job satisfaction. There were 45 responses from graduates with a preference for FP and 35 responses from graduates with a preference for TJ. This paper presents the results from this alumni survey and compares the responses and comments from the engineers with the minority FP ( feeling, perceptive) preference with those from the engineers with the majority TJ (thinking, judging) preference. Introduction National engineering groups both in Canada1 and in the United States2 remind us that engineering is changing faster than ever before. Accelerating programs in R&D together with the information technology explosion are leading to a "fusion of technologies" and the " merging of once disparate fields". This is leading to an engineering employment shift from manufacturing to service industries and from large co Bob Sternmpanies to smaller engineering enterprises. Such changes place added demands on engineers. Not only must they preserve a broad technical base with the ability to develop and transfer their expertise to other areas but they must also be able to cope with technical and societal change involving a variety of social, political and environmental factors. What are the personality characteristics in tomorrow's engineers that would naturally equip them to deal with such novel technical demands and interpersonal challenges?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".