MétaCan
Menu
Back to cohort
Record W2409741560 · doi:10.1177/0001839216653712

Explaining the Selection of Routines for Change during Organizational Search

2016· article· en· W2409741560 on OpenAlexaffabout
Amit Nigam, Ruthanne Huising, Brian Golden

Bibliographic record

VenueAdministrative Science Quarterly · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)InterdependenceOrganizational changePoliticsPlanned changePublic relationsSelection (genetic algorithm)Political scienceComputer science

Abstract

fetched live from OpenAlex

We examine how organizations select some routines to be changed, but not others, during organizational search. Selection is a critical step that links an exogenous trigger for change, change in individual routines, and larger processes of organizational adaptation. Drawing on participant observation of an initiative to improve perioperative efficiency in seven Ontario hospitals, we find that organizational roles shape selection by influencing both politics and frames in organizational search. Roles shape politics by defining the role-specific goals of the people who have authority to change a routine. Organizations will not select a routine for change unless at least some elites—people with role-based authority—frame the existing routine as negatively affecting their role-specific goals. Roles also shape individuals’ frames. Because people are only partially exposed to interdependencies between routines in their day-to-day work, they may not be fully aware of the diverse impact that an existing routine can have on their goals. Proponents for change can use strategic framing to focus attention on interdependencies between routines to get elites to better see how an existing routine negatively affects their goals. They can also change elites’ goals by using strategic framing to focus attention on new and broader goals that the change in routine would promote.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0040.009
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.054
GPT teacher head0.293
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations80
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAdministrative Science QuarterlySame topicManagement and Organizational StudiesFrench-language works237,207