Alliance Network Advantage: At the Frontiers of Research and Teaching
Bibliographic record
Abstract
This symposium will contain presentations to reflect recent advances in research and teaching on alliances. The leading scholars in the field will examine how firms manage alliance dissolution, how firms learn to manage alliances and what do managers responsible for realizing competitive advantage from alliance networks actually do. The last presentation will contain insights from the book that aims at converting academic knowledge on alliance management into managerially relevant tools that can be used in the classroom and applied by business executives in their daily work. Partnering Experience and Alliance Performance Presenter: Dovev Lavie; Technion Israel Institute of Technology What do Alliance Managers Do? Presenter: Yves Doz; INSEAD Network Advantage: How to Unlock Value from Your Alliances and Partnerships Presenter: Henrich R. Greve; INSEAD Presenter: Tim Rowley; U. of Toronto Presenter: Andrew V. Shipilov; INSEAD After the Break-Up: The Relational and Reputational Consequences of Withdrawals from VC Syndicates Presenter: Ranjay Gulati; Harvard U. Presenter: Pavel Ivanov Zhelyazkov; Harvard U.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".