Bibliographic record
Abstract
The evolution of interorganizational networks is shaped by micro and macro processes. At the micro level organizational dyads negotiate relationships in light of their own cost-benefit analysis. At the macro level resources flow through networks and are mobilized by coalitions. Current research is beginning to examine integrating mechanisms which link network dynamics to dyadic relationship formation. In this paper we examine interorganizational brokerage as an integrating mechanism linking micro and macro network processes. We focus on the formation of networks in the global television industry. The Children's Television Network (CTW) has licensed and co-produced its flagship program Sesame Street in many countries around the globe. Recently, it has expanded beyond a strategy based on direct first-order linkages to one of brokerage and interorganizational entrepreneurship, entailing the formation of second-order linkages--linkages between organizations with which CTW has direct first-order relationships. In the aftermath of the Oslo Peace Agreement, CTW acted as a broker and sponsor of a joint venture between Israeli and Palestinian broadcasters. The main challenge facing CTW was a high degree of distrust between the parties motivated by fears of opportunistic exploitation. Such fears typically result in high transaction costs, making joint venture formation difficult, if not impossible. In its capacity as broker, CTW worked to reduce transaction costs. This was done by providing resources where needed, and by facilitating interaction and trust building between the parties. We describe CTW's tactics during the formation of this joint venture, and we analyze the outcome of the joint venture from the point of view of CTW's overall strategy. We conclude by discussing CTW's network and brokerage strategy in the aftermath of the joint venture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| 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".