Bibliographic record
Abstract
Although electronic face-to-face meetings are increasingly being used by organizations to improve the productivity of their strategic planning teams, design task forces, quality circles, sales management, and other organizational groups (Alavi, 1993; Dishman & Aytes, 1996), the rate of adoption of the technologies to support these meetings appears to be slowing (Grise & Gallupe, forthcoming). A possible reason for this reduced rate of adoption may be the difficulty in training competent electronic meeting facilitators. These facilitators play a key role in electronic meetings that use computer-based group technologies or group support systems (GSS) to assist the group in tasks such as generating ideas, evaluating alternatives and developing action plans. The purpose of this chapter is to describe how an action learning approach was used to train traditional meeting facilitators in the tools, techniques and processes of electronic meeting facilitation. This chapter begins with a description of action learning, in particular the three schools of action learning. The second section explains the nature of the “experiential” school of action learning and the GSS facilitation training program used in a research project in which 15 facilitators, already experienced in conventional meetings, were trained to become facilitators of electronic meetings. The final sections describe some lessons learned and implications for organizations training their electronic meeting facilitators.
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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 | 0.001 | 0.001 |
| 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".