Vicariously Experiencing it all Without Going Outside
Bibliographic record
Abstract
The livestreaming industry in China is gaining greater traction than its European and North American counterparts and has a profound impact on the stakeholders' online and offline lives. An emerging genre of livestreaming that has become increasingly popular in China is outdoor livestreaming. With outdoor livestreams, streamers broadcast outdoor activities, travel, or socialize with passersby in outdoor settings, often for 6 or more hours, and viewers watch such streams for hours each day. However, given that professionally produced content about travel and outdoor activities are not very popular, it is currently unknown what makes this category of livestreams so engaging and how these techniques can be applied to other content or genres. Thus, we conducted a mixed methods study consisting of a survey (N=287) and interviews (N = 20) to understand how viewers watch and engage with outdoor livestreams in China. The data revealed that outdoor livestreams encompass many categories of content, environments and passersby behaviors create challenges and uncertainty for viewers and streamers, and viewers watch livestreams for surprising lengths of time (e.g., sometimes more than 5 continuous hours). We also gained insights into how live commenting and virtual gifting encourage engagement. Lastly, we detail how the behaviors of dedicated fans and casual viewers differ and provide implications for the design of livestreaming services that support outdoor activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".