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Record W2605399215 · doi:10.5465/amj.2014.0842

Design Performances: How Organizations Inscribe Artifacts to Change Routines

2017· article· en· W2605399215 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

VenueAcademy of Management Journal · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArtifact (error)PerformativityGenerativityAgency (philosophy)Computer scienceSociotechnical systemEnforcementKnowledge managementDynamics (music)PatrollingAction (physics)Human–computer interactionSociologyArtificial intelligenceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Organizations often create and employ artifacts in order to change their routines, but little is known about how artifacts can be designed to intentionally influence routine dynamics. In this paper, I present findings from an inductive, ethnographic study of how a law enforcement agency fabricated a game-theoretic artifact to modify its patrolling routine. Based on my in-depth analysis of the actions associated with creating this game-theoretic artifact, I develop a theoretical model that shows how organizational actors iteratively engage in a series of design performances to envision new sociomaterial assemblages of actors, artifacts, theories, and practices. These design performances influence routine dynamics by both eliciting mechanisms of abstracting grammars of action, exposing assumptions, distributing agency, and appraising outcomes, and by creating new assemblages that can be deployed in future routine performances. By revealing the generativity of design performances and sociomaterial assemblages, this empirical study contributes to our understanding of routine dynamics, performativity, and strategy tools.

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: Methods · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0030.001
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 head0.092
GPT teacher head0.310
Teacher spread0.218 · 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