Bibliographic record
Abstract
Very little has been written about the crucial role of the moderator in public discussion forums or discursive communities. Group theory tends to draw upon group experiences from non-moderated groups such as criminal juries or groups convened for the purpose of observation. Therefore group theory is concerned with group members’ behaviour that is not affected by intervention by someone with the overall process in mind. Practicing moderators and process designers understand the importance of this role in face-to-face consultation. The translation of these skills into an online environment is the subject of this article. Unfortunately those who write about e-democracy rarely mention this important function, focusing instead on the technology, even though the moderator role is increasingly employed, for example in online collaboration or decision-making. The role of the e-moderator or e-convenor has attracted some attention, both in public deliberation circles (for example, National Issues Forums in the U.S.) and tertiary education (Salmon, 2002). Understanding e-moderation requires an appreciation of moderation per se. This article draws on input from a network of professional facilitators (in Australia, Canada, the United States, and the UK) who were asked by the author (in November 2004) to describe the qualities of an effective facilitator/moderator in a face-to-face (F2F) environment. Their combined responses, previously unpublished data, are used in this article. This primary data is combined with the author’s own critical reflections based on 20 years of experience as a group facilitator and is integrated with the writings of theorists and practitioners.
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.
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 | 0.066 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".