Introduction : How does novelty emerge?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".