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Record W2140900423 · doi:10.1504/ijmc.2007.011492

Identifying the differences between stationary office support and mobile work support: a conceptual framework

2006· article· en· W2140900423 on OpenAlexaff
Wuping Zheng, Yufei Yuan

Bibliographic record

VenueInternational Journal of Mobile Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWork (physics)Computer scienceMobile business developmentMobile computingMobile technologyConceptual frameworkContext (archaeology)Knowledge managementMobile telephonyProcess managementMobile WebTelecommunicationsBusinessMobile radioEngineering

Abstract

fetched live from OpenAlex

The rapid development of mobile technologies provides great potential to support mobile work that was not supported by traditional stationary information systems. To realise this great potential, it is important for us to fully understand the nature of mobile work in order to develop efficient and effective support for mobile workers. In this paper, we propose a conceptual framework and use this framework to analyse four fundamental aspects of mobile work: mobile workers, mobile tasks, mobile context and mobile technology. The key differences between office work support and mobile work support are highlighted. The conceptual framework can be used to identify research issues and provide guidelines for the development of effective mobile work support systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0030.021
Scholarly communication0.0130.015
Open science0.0030.007
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.061
GPT teacher head0.336
Teacher spread0.274 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations51
Published2006
Admission routes1
Has abstractyes

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