An empirical study of facilitation of computer-mediated distributed requirements negotiations
Bibliographic record
Abstract
Group facilitation is an important element of group approaches to requirements engineering (RE). The facilitation in traditional face-to-face groups is challenged by the increased globalization of the software industry. Thorough empirical investigation of human facilitation in computer-mediated requirements meetings is needed. This paper presents findings about the facilitation of distributed group settings in a controlled environment. Three professional facilitators mediate 15 three-person groups negotiating software requirements. Facilitation in face-to-face meetings is contrasted with four group settings in which the facilitator is physically separated from the group or co-located with key stakeholders. Rich qualitative and behavioral data enables an understanding of differences and similarities in the facilitation of the distributed groups and of aspects that were detrimental or beneficial to their facilitation. The empirical evidence indicates a reduced richness of social behaviors in computer-mediated group settings which: made the group facilitation problematic; but also enabled certain facilitation support in the medium itself.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".