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Record W1543933934

Dynamic Capability Deployment among U.S. Defense Systems Integrators as a Response to Environmental Change

2013· preprint· en· W1543933934 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

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2013
Typepreprint
Languageen
FieldEngineering
TopicMilitary Strategy and Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsSoftware deploymentOrchestrationAsset (computer security)Computer scienceDynamic capabilitiesIntegratorProcess (computing)Set (abstract data type)Environmental changeProcess managementComputer securityClimate changeBusinessTelecommunicationsKnowledge managementSoftware engineeringOperating systemEcology
DOInot available

Abstract

fetched live from OpenAlex

We conduct a longitudinal case study of the top five U.S. defensesystems integrators between 1998 and 2007 to examine their responseto the massive environmental change triggered by the 9/11 attacks. Wecollected and organized data around a set of fine-grained measuresto analyze, over time and across firms, top management attention tochange in the environment, discourse about firm-level change as wellas how firms actually renew their assets at multiple levels. We find thatthe process of dynamic capability (DC) deployment unfolds in three steps,from the recognition that the environment has changed (monitoringand sensing), to the decision to deploy DC (analyzing and deciding)and to the implementation of asset re-orchestration (implementing).Methodological, theoretical and practical implications are discussed.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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