Exploring a Real-time Feedback Display of Non-verbal Cues in Online Work Meetings to Support Self-Presentation
Bibliographic record
Abstract
Expressing oneself appropriately in online meetings through non-verbal cues can be challenging for knowledge workers. Automatic non-verbal cue detection technologies have the potential to support workers' self-presentation efforts through real-time feedback, but little is known about workers' reactions to and the implications of doing so. We designed and implemented Novecs as a technology probe of a real-time feedback display that automatically detects and signals users' own non-verbal cues -- smiling, nodding, gaze, and posture. Novecs was deployed in an exploratory field study (n=18) to support knowledge workers' self-presentation in their everyday meetings. Post-study interviews reveal how Novecs' real-time feedback helped increase in-the-moment self-awareness, and how neutrally-framed feedback may help navigate tensions between authentic and in-authentic self-presentation. Participants also emphasized the need for natural timing when adjusting non-verbal cues in-meeting. We discuss design opportunities and challenges of real-time, non-verbal cue feedback systems, such as personalizing feedback based on different meeting types.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".