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Interactions as the Micro-Dynamics of Institutional Processes

2015· article· en· W2796319441 on OpenAlexaboutno aff
Sahlin Andersson

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionalisationDynamics (music)SociologyMechanism (biology)Meaning (existential)Political scienceEpistemologyPedagogy

Abstract

fetched live from OpenAlex

Although there has been a long-standing realization of the importance of interactions in institutional theory, typically, research has not focused on the unique contribution of this level of analysis. In our symposium we target interactions as a mechanism which contributes to our understanding of the process of institutionalization and institutional change. We focus specifically on the patterns of micro level interactions; the interplay between micro level interactions and macro level change and the way meaning is negotiated and constructed through interaction. We include four empirical as well as one theoretical paper and bring together a number of different streams in institutional theory. Following the presentations, Sahlin Andersson, an influential and major contributor to institutional theory will serve as a discussant. Interactions as Micro-Dynamics of Institutional Processes Presenter: Chad McPherson; U. of Iowa Configuration and Communitas in Institutional Change Presenter: Gazi Islam; Grenoble Ecole de Management Presenter: Charles-Clemens Rüling; Grenoble Ecole de Management Presenter: Elke Sybille Schüßler; Free U. Berlin Interactions as a Mechanism of Translation Presenter: Tamar Gross; Hebrew U. of Jerusalem Confronting Inequality and Overcoming Institutional Barriers Presenter: Johanna Mair; Hertie School of Governance Presenter: Miriam Wolf; Hertie School of Governance Presenter: Christian Seelos; Stanford U. Risk Talk and Institutional Change: The Case of Industrial Chemicals Presenter: Cynthia Hardy; The U. of Melbourne Presenter: Steve Maguire; McGill U.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.208

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 communication0.0000.000
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 head0.058
GPT teacher head0.329
Teacher spread0.270 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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