Membership and Incentives in Network Alliances
Bibliographic record
Abstract
We propose a general and precise model of a network alliance that addresses both the role of membership and the role of incentives in the coordination of actions and interactions of network alliance members. Using examples in such disparate industries as professional engineering, accounting services, and commercial fueling as the basis of our model, we show that a commission fee chosen by the network provider can be combined with a classical exclusivity agreement-which does not restrict where members recruit customers, while at the same time protecting the members' locations where customers are served-to motivate increases in member investment and, consequently, in network profits. We also show that the most profitable network size emerges naturally. That is, the most profitable network size restricts membership, and emerges as a consequence of the exclusivity agreement and the setting of the commission fee. Our results require that members' investments are more valuable with increases in other members' investments, that prospective members are sufficiently different that there is an adequate range in the business potential of members, and that the effect of other members' investments on a given member's business potential is moderately low.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".