A Case-based Reasoning Approach for Automated Facilitation in Online Discussion Systems
Bibliographic record
Abstract
Online discussion systems have recently attracted great attention as an enabling approach of realizing collective intelligence. During online discussions, human facilitators are introduced in order to help these discussions to proceed more efficiently and productively. However, there are a number of challenges such as human bias and time restriction that need to be solved in the human facilitator-based online discussion systems. As a result, automated facilitation becomes necessary in order to overcome these shortcomings. This paper proposes a novel approach for automated facilitation that utilizes case based reasoning (CBR) in order to imitate the human facilitator thinking style. The proposed approach works in issue based information system (IBIS) discussion style where complex problems are designed as a conversation amongst several stockholders. These stockholders, in turn, bring their expertise in order to resolve the discussion point. Experimental results show the ability of the proposed approach to improve the performance of online discussion systems, and to guide the online discussion towards consensus and towards gathering wisdom efficiently.
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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 | 0.000 | 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".