Building a Car While Driving It: How Nascent Orchestrators Build Identity While Growing Ecosystems
Bibliographic record
Abstract
Growing attention to ecosystems has led to an interest in how these complex systems of interdependent organizations emerge. Orchestrators – organizations that align disparate actors around shared goals, enabling collaboration, resource sharing, and knowledge dissemination – are central drivers of ecosystem development. Despite their importance, little is known about how these organizations develop. Through an inductive, qualitative study of a nascent orchestrator in an emerging social enterprise ecosystem, we examine how orchestrators simultaneously develop ecosystems and their own organizational identities (OI). We found that as the orchestrator engaged in ecosystem-building activities and gained legitimacy, OI confusion – a fundamental lack of clarity regarding “who we are” and “what we do”– emerged and intensified. OI confusion undermined the orchestrator’s coordination efforts, threatening ecosystem development. To address this confusion, the orchestrator engaged in “polyphonic OI work” – allowing multiple interpretations of OI to coexist. While this approach allowed the orchestrator to continue coordinating ecosystem activities, OI confusion continuously resurfaced, diverting attention and resources away from ecosystem work. These findings suggest that orchestrators occupy a unique role which requires their identity to constantly co-evolve with the ecosystem they are developing; the tensions that arise from this co-evolution must be managed for orchestrators to successfully develop an ecosystem.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".