Blind Dates and Arranged Marriages: Longitudinal Processes of Network Orchestration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using longitudinal qualitative and network data capturing five years of evolution of an interorganizational network, this paper explores network orchestration – the process of assembling and developing an interorganizational network. In particular, we analyze shifts in the network orchestrator’s actions and the network’s structure and composition. We find that an orchestrator builds the capacity to assemble a network over time through the accumulation of resources and specialized expertise. However, as the network develops, an orchestrator faces an evolving set of dilemmas arising from the need to demonstrate value for various members and audiences. To resolve these dilemmas, orchestrators may shift their actions, moving from initially encouraging serendipitous encounters between network members (“blind dates”) to increasingly selecting members and more closely influencing their interactions (“arranging marriages”). We discuss implications of our findings for a processual understanding of orchestrated network assembly and growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it