Bibliographic record
Abstract
The rise of smartphones in the past decade has created situations in which individuals use them in public and private domains. More recently there has been an increase in the adoption of smartphones by corporations; what is not very well understood is their use within meetings. In this dissertation I present quantitative and qualitative data from two online surveys conducted two years apart on the type of smart mobile devices used in meetings, and the attitudes and behaviours of meeting participants towards their usage. The results from the two surveys included four key findings: (1) meeting participants believed that multitasking with a mobile device was a commonly adopted activity; (2) participants took a more accepting attitude towards using certain mobile devices (specifically laptops) in meetings; (3) it was somewhat acceptable to make work-related calls or send text messages regarding work-related emergency matters using smartphones during meetings; and (4) individuals in management tended to think that making important work-related calls during meetings was acceptable. Furthermore, from a list of six types of departments, the operations department tended to rate texting important work-related messages during meetings as acceptable compared with other departments. After reviewing the data from surveys I and II, it was determined that more detailed data were required to observe people’s actual behaviours in live meetings. As a result, a study was devised to simulate a meeting scenario in which one individual would receive and send text messages. Eight video recordings of meeting participants were captured and analyzed to assess their resulting attitudes and behaviours. In four of the meetings text messages arrived in two clusters (i.e., five text messages at the beginning and three at the end of the meeting), while for the remaining four meetings text messages arrived evenly distributed throughout the meeting. The data from those meetings suggest that the participants in the evenly distributed text messages group of meetings interacted with their mobile devices more often but on a less obtrusive level by checking their phone status. The participants in the clustered grouping of text messages group of meetings tended to produce more negative comments (verbal and non-verbal) regarding the actor and their own phone usage. When the actor received a text message, participants tended to give a negative non-verbal gesture, such as gazing at him, or when participants used their own mobile phones they tended to provide a verbal justification of their own use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".