MétaCan
Menu
Back to cohort
Record W2096244445 · doi:10.1145/1460563.1460635

The effects of local lag on tightly-coupled interaction in distributed groupware

2008· article· en· W2096244445 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLagComputer scienceCollaborative softwareHuman–computer interactionTime lagDistributed computingFace (sociological concept)Knowledge managementComputer network

Abstract

fetched live from OpenAlex

Tightly-coupled interaction is shared work in which each person's actions immediately and continuously influence the actions of others. Tightly-coupled collaboration is a hallmark of expert behavior in face-to-face activity, but becomes extremely difficult to accomplish over distributed groupware. The main cause of this difficulty is network delay that disrupts people's ability to synchronize their actions with another person. In this paper we report on two studies that explore local lag as a way of reducing this problem. When applied to visual feedback, local lag synchronizes the visual environments of the local and remote clients, preventing one person from getting ahead of the other. We tested the effects of local lag in several delay conditions: we found that the technique significantly improved performance, and that users did not rate local lag as more difficult or frustrating to use. Our studies improve our understanding of local lag and of how it improves tightly-coupled interaction in distributed groupware.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.392
Teacher spread0.265 · 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