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Record W4366587900 · doi:10.1145/3544548.3581141

Challenging but Connective: Large-Scale Characteristics of Synchronous Collaboration Across Time Zones

2023· article· en· W4366587900 on OpenAlexaff
Lillio Mok, Lu Sun, Shilad Sen, Bahareh Sarrafzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultinational corporationLimitingTime zoneComputer scienceScheduling (production processes)BusinessGeographyEngineeringOperations management

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.010
GPT teacher head0.312
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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