The emergence of a theoretical framework for gss facilitation: The dualities of e‐facilitation
Bibliographic record
Abstract
Electronic meeting facilitation (e‐facilitation) continues to be a critical success factor in the use of information technology to support face‐to‐face collaborative work. Yet researchers and practitioners continue to struggle to understand the subtleties and difficulties in the application of meeting facilitation techniques in the ‘electronic’ context. To clarify that understanding, this paper develops a new theoretical framework that examines how technology interacts with human facilitator behavior in an electronic group meeting. This framework, The Dualities of E‐Facilitation, is composed of two dualities: the Duality of Computer and Human Interaction, and the Duality of Routine and Intuitive Actions. The framework emerged from an analysis of the e‐facilitation behaviors of newly trained face‐to‐face electronic meeting facilitators.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".