Dialogue Management Factors: A 2010 Vancouver Winter Olympic and Paralympic Games Case
Bibliographic record
Abstract
Dialogue is a process of inquiry and learning that is based on openness, listening, developing meaning, and sharing knowledge through conversation. It is a collaborative approach to discussion that seeks to build awareness, challenge assumptions, and reach deeper understandings of issues. From a pedagogical perspective, it has the potential to be a useful alternative to more traditional teaching methods—especially with respect to helping students develop their communication, critical thinking, and analytical skills. While the importance of employing various dialogic management principles in various applied dispute resolution settings is well documented in the literature, little empirical evidence concerning the relative worth of such factors in teaching environments exists. This is especially the case in tourism and hospitality teaching contexts. This article identifies the perceived worth of these factors in shaping dialogues tied to the conflicted and complex topic of hallmark event management. It uses the perspectives of university students participating in a unique “Semester in Dialogue” program focusing on Vancouver's forthcoming 2010 Winter Olympic and Paralympic Games to measure the value of these factors. The findings suggest that (a) specific management practices related to organizing, planning, and moderating dialogues can create useful learning opportunities for students and other civil society members and (b) well managed dialogues are effective teaching vehicles not only for exploring complex tourism management issues but also for engaging students in discussions that push participants from firmly held positions to new territories of shared learning and mutual understanding.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".