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Record W3083987809 · doi:10.7202/1066072ar

Boundary Objects as the Missing Link in the Orchestration of Resources: An Exploratory Study of Dassault Aviation Mirage IV and Rafale Programs

2019· article· en· W3083987809 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement international · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsnot available
FundersMinistry of DefenseMinistère de la Défense
KeywordsOrchestrationBoundary objectComputer scienceBoundary (topology)Relation (database)AviationEngineeringAerospace engineeringData miningPolitical scienceMathematicsNegotiationLaw

Abstract

fetched live from OpenAlex

In this contribution, we investigate the use of boundary objects (Star and Griesemer) for the orchestration of resources (Teece). We propose a comparative case study elaborating on two Dassault Aviation military fighters under an abductive approach. In this contribution, we elaborate on the micro-foundations approach. Our contribution discusses several properties of boundary objects in relation with the orchestration of resources: type, granularity, openness, malleability, and completeness. We conclude that boundary objects are critical to orchestration. Their properties explain why they diversely impact on sensing, seizing and reconfiguring. They elaborate on knowledge articulation and teamwork, and require specific ways of working.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.270

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 communication0.0000.001
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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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