Challenging but Connective: Large-Scale Characteristics of Synchronous Collaboration Across Time Zones
Bibliographic record
Abstract
Organizations are becoming increasingly distributed and many need to collaborate synchronously over great geographical distances. Despite a rich body of literature on spatially-distanced meetings, gaps remain in our understanding of temporally-distanced meetings. Here, we characterize cross time zone collaborations by analyzing 20 million meetings scheduled at a multinational corporation, Microsoft, supported by a survey on how 130 employees perceive their scheduling needs. We find that cross time zone meetings are closely associated with scheduling patterns around early morning and late evening hours, which are challenging and discordant with employees’ stated temporal preferences. Additionally, the burdens of meeting across time boundaries are asymmetrically distributed among workers at different levels of the organization and different geolocations. Nonetheless, we further observe evidence that cross time zone attendees are organizationally distant and diverse, suggesting that addressing these challenges by limiting meetings would disafford employees the opportunities to connect. We conclude by sharing opportunities for facilitating cross time zone meetings that foster healthier global collaborations.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".