Flower-Pop
Bibliographic record
Abstract
We explore the potential use of mobile devices as a collaborative sensing system that can proactively mediate casual group conversations. In this study, we aim to investigate (i) the impacts of a mobile system's passive and active conversation facilitation and (ii) the ways in which sociocultural aspects that affect casual group conversation should be considered in the design of proactive mobile systems. Toward this goal, we developed Flower-Pop, a mobile system that monitors group conversations and visualizes interaction patterns using metaphorical expressions based on blossoms. This system provides passive facilitation as well as active facilitation modes such as proactive conversation visualization and photo sharing. The active modes can encourage inactive participants to share photos and select random people to speak. Focusing on Korea, our field study showed that Flower-Pop's mediation created smooth topic/speaker transitions and encouraged less-active speakers to better engage in group conversation. We also found that the sociocultural aspects of casual group conversation, such as the location's characteristics, social relations, and the group's interests, affected participants' use of the Flower-Pop system. Based on our findings, we discuss methods for designing mobile systems for conversation facilitation and outline how opportune sociocultural factors could be identified based on mobile devices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".