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Record W3121990389 · doi:10.5167/uzh-173253

On the risk of studying practices in isolation: Linking what, who, and how in strategy research

2015· article· en· W3121990389 on OpenAlexaff
Paula Jarzabkowski, Sarah Kaplan, David Seidl, Richard Whittington

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLimitingPerspective (graphical)Isolation (microbiology)Best practiceResource (disambiguation)Focus (optics)Knowledge managementPolitical sciencePublic relationsSociologyBusinessManagement scienceComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

This article challenges the recent focus on practices as stand-alone phenomena, as exemplified by the so-called “Practice-Based View of Strategy” proposed by Bromiley and Rau. While the goal of “Practice-Based View of Strategy” points to the potential for standard practices to generate performance differentials (in contrast to the resource-based view), it marginalizes well-known insights from practice theory more widely. In particular, by limiting its focus to practices, that is, “what” practices are used, it underplays the implications of “who” is engaged in the practices and “how” the practices are carried out. In examining practices in isolation, the “Practice-Based View of Strategy” carries the serious risk of misattributing performance differentials. In this article, we offer an integrative practice perspective on strategy and performance that should aid scholars in generating more precise and contextually sensitive theories about the enactment and impact of practices as well as about critical factors shaping differences in practice outcomes.

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 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)0.002
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 score0.089
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.093
GPT teacher head0.273
Teacher spread0.180 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2015
Admission routes1
Has abstractyes

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