A comparative analysis of meeting participant perception and use of smartphones and other mobile devices during meetings
Bibliographic record
Abstract
In the past decade, as the processing power of smartphones has increased, users have started to use them within a business environment, specifically in corporate meetings in exchange for their laptops or tablet computers. The use of smartphones and other mobile devices in meetings may be causing a positive or negative change in the attitudes and behavior of meeting participants. An online survey was created and deployed in second half of 2010 to investigate how and for what purpose meeting participants use mobile devices. The survey also collected data on the perception and behavior of meeting participants when others use their mobile device in meetings. A second survey with similar questions was deployed in late 2012 to compare whether attitudes and behavior of meeting participants had changed in the two year period. Statistical and descriptive analyses were conducted to examine any differences in responses between the first and second survey. Major findings from the comparison of the two surveys include: individuals have become more accepting of having and using laptops and feature phones in their meetings. In addition, organizations have become more supportive of employees using iPhones.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".