Evaluating the Capacity for Work Chat Apps to Maintain and Facilitate Workplace Communication During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has caused a huge shift in the workforce as many former office employees began working remotely. Remote work, or "teleworking," was not as prevalent prior to the pandemic as it is now (StatCan, 2021). Research prior to the pandemic found that teleworking relied on communication technologies to connect the employee to the workplace and led to greater employee autonomy, satisfaction and in many cases productivity (Baruch, 2000). Research on remote work during the pandemic has so far found greater instances of stress and isolation as a result of remote work (Wang, Liu, Qian, Parker 2021). The literature also highlights the distance between workers in the virtual environment as well as disruptions in communication. Informed by both the pre-pandemic and current literature on remote work, this paper will investigate the potential of work chat applications such as Microsoft Teams, Slack and Discord to improve communication and worker engagement. The paper aims to improve understanding of the ways in which remote workers uses and experience chat applications in day-to-day, informal communication that takes place outside of formal meetings. This will be achieved by surveying remote workers about their experience using chat technologies and the benefits and challenges that stem from these technologies.
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 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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".