Why do we need personality diversity in software engineering?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diversity of skills is good for society, it is also good in problem solving because different people see a problem from several pers-pectives, so diversity should be good for software engineering too. This study tackles a difficult to study aspect of software engineer-ing, that is, how to best associate personnel with the various tasks in a software project. The approach uses psychological types to determine who is best suited to particular development roles. The article has four main objectives: (1) to arouse awareness of human factors among software engineers; (2) to investigate how psycho-logical factors can contribute to their effectiveness at work; (3) to catalyze effort among software engineers leading towards a deeper understanding and broader applications of human factors in the light of the activities involving the engineering of software; and (4) to emphasize the important of skill diversity in the software engi-neering field. This article provides conceptual knowledge, reports findings, and presents both real and hypothesized beliefs from the software engineering community. Likewise, it is hoped that the article will motivate software engineers and psychologists to con-duct more research in the area of software psychology, so as to understand more profoundly the possibilities for increased effec-tiveness and fulfilment among software engineers
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.
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 | 0.001 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 it