A Hole in the Wall: The Potential of Persistent Video-enabled Communication Channels to Facilitate Collaboration in Dispersed Teams
Bibliographic record
Abstract
With advances in telecommunications and information technology, collaborations and teamwork are no longer bound by geography. However, challenges stemming from distance must be managed to ensure that teams work together successfully. One of the primary challenges is finding ways to facilitate communication and coordination across distance and time. Skype, Zoom, and other internet-enabled tools provide some potential to accomplish this; however, relatively few studies have been completed on the best ways to use a continuously open communication channel to facilitate teamwork within a geographically dispersed collaboration. This study contributes to this discussion by examining the use of such a channel by a dispersed lab. While this paper suggests the potential for similar collaborations, open audio and video communication channels can create the sense of social presence by reminding members that they are part of larger efforts, even when working at a distance. It managed to do so while addressing concerns of privacy and a potential for surveillance culture. These tools also complement the other well-established online ones as well as face-to-face meetings for project coordination and decision-making.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".