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Record W2946902229 · doi:10.4018/ijec.2018010101

Using IOS in a Collaborative Way

2018· article· en· W2946902229 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

VenueInternational Journal of e-Collaboration · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsAffordanceKnowledge managementContext (archaeology)Identity (music)BusinessInformation systemProcess managementComputer scienceEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Interorganizational systems (IOS) are information and communication technology-based systems that transcend organizational boundaries. However, their use does not always lead to successful interorganizational collaboration, particularly in settings where significant changes in business processes are needed. The architecture, engineering and construction (AEC) industry offers such a setting, in particular as its stakeholders are encouraged to use of a novel type of interorganizational system known as building information modeling (Building Information Modelling), which can only be successfully used if parties collaborate. This research seeks to uncover what leads to interorganizational collaboration in this particular context. Drawing on rich data from interviews with BIM users involved in interorganizational projects, the authors propose a conceptual model of how interorganizational collaboration unfolds. The authors highlight the central role played by interorganizational infrastructure, collective identity, and IT affordances, on interorganizational collaboration.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.034
GPT teacher head0.426
Teacher spread0.392 · 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