Network Horizon and the Dynamics of Network Positions: A Multi-Method Multi-Level Longitudinal Study of Interfirm Networks
Bibliographic record
Abstract
Diederik van Liere was born in Kortenhoef, the Netherlands, on February 18, 1978. He attended Herman Jordan Lyceum in Zeist, from which he received his Atheneum diploma in 1996. After high school, Diederik went on to study Business Administration at the Erasmus University Rotterdam, the Netherlands. In 1997, he founded his own Internet design company and was an exchange student at Brandeis University, Boston, United States, in 2000. In November 2002, Diederik received his Master’s degree with a thesis on how the Internet transforms value chains into value networks. \nIn November 2002, Diederik became a PhD student at the Department of Decision and Information Sciences at RSM Erasmus University. His work has been published in Journal of Information Technology, Decision Support Systems and Production, Planning, and Control. Furthermore, a book chapter will appear in the Network Strategy - Advances in Strategic Management series. In January 2006, Diederik went to the Rotman School of Management at the University of Toronto as a visiting researcher. He has presented his research at major international conferences, such as the Academy of Management (AoM), Sunbelt, and European Conference on Information Systems. Moreover, he won the Best Reviewer Award for the Organization, Management, and Theory (OMT) Division of the Academy of Management in 2006 and currently serves as a member of the OMT Research Committee. Furthermore, Diederik serves as a reviewer for the Business Process and Strategy (BPS) Division of the AoM. His research interests focuses on dynamics of interfirm networks, network strategies, network cognition, and competitive rivalry within and between interfirm networks.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".