Theorizing Institutional Entrepreneuring: Arborescent and rhizomatic assembling
Bibliographic record
Abstract
A growing body of research has cataloged the myriad actors involved in tackling persistent institutional problems. Yet we lack a theoretical toolkit for explicitly conceptualizing and comparing diverse modes of institutional entrepreneuring—the processes whereby actors are created and equipped for institutional action—capable of ameliorating grand challenges. Drawing on assemblage theory, we articulate two ideal-typical modes of assembling actorhood: arborescent and rhizomatic. We differentiate each mode along four principles: association, combination, division, and population. Building on our theorization, we propound an arborescent-rhizomatic space comprising clusters of arborescent, rhizomatic, and hybrid actorhood. To explore the generativity of our framework, we revisit selected research at the intersection of institutional entrepreneurship and grand challenges. We close by articulating how our concept of assembling actorhood reorients research toward institutional entrepreneur ing and contributes to the application of assemblage theory within organization studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".