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Record W238923386

Introduction : How does novelty emerge?

2015· book-chapter· en· W238923386 on OpenAlexaff
Raghu Garud, Barbara Simpson, Ann Langley, Haridimos Tsoukas

Bibliographic record

VenueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2015
Typebook-chapter
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsNoveltyTheme (computing)Variety (cybernetics)EpistemologySpace (punctuation)SociologyProcess (computing)Cognitive sciencePsychologyComputer scienceSocial psychologyPhilosophyArtificial intelligenceLinguistics
DOInot available

Abstract

fetched live from OpenAlex

In this introductory chapter, we clarify how contemporary organizational scholars view emergence and novelty. As the use of these terms has grown, their meanings have become increasingly diffuse. In response, we explicate three lenses that tease out core distinctions between different philosophical and theoretical approaches to studying emergence. Each of the three lenses, which we call respectively spatial emergence, relational emergence, and temporal emergence, is based on a unique pairing of underlying assumptions about the exogenized or endogenized nature of both space and time. Each also has particular implications for practitioners seeking practical insights into emergence, and for researchers conducting studies on emergence. In addition, we introduce the chapters in this volume, which offer a variety of philosophical, theoretical, and empirical perspectives on the theme of novelty emergence. In introducing the chapters in Part I, which engage directly with this theme, we emphasise the interplay between the three lenses. The remaining chapters in Part II address developments more generally in the domain of process organization studies.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.269
Teacher spread0.178 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations10
Published2015
Admission routes1
Has abstractyes

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