Toward a Model of Emotional Contagion Influence on Agile Development for Mission Critical Systems
Bibliographic record
Abstract
This position paper provides a framework to test positive and negative emotional contagion between agile teams for producing mission critical systems in order to enhance agile teams' cooperativeness and performance, lower conflicts, and to make decisions more accurate. Due to human errors in analyzing, designing, implementing, and testing phases for producing mission critical systems, losses and risks are significantly higher than other systems; while the adoption of agile development processes in mission critical systems has shown promising results of minimizing risks and costs. However, agile processes are people-oriented where human error is the main contributor to the success or failure of the system. Within the software industry, studies have investigated developers' conation and cognition to enhance their performance and communication within teams, while the role of affect (emotions and moods) was neglected for decades. Emotional contagion, as a factor of affect influence, has only been tested in studies at the managerial decision-making level; while no evidence of such studies that investigate whether or not the emotional contagion influences behavioural groups in agile developments which is the main concern of this paper.
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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 | 0.001 |
| 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 | 0.001 | 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".