Drivers of Governance Modes and Reconfiguration
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
This symposium seeks to shed light on what drives firms to engage in different governance modes. Four papers comprise this symposium, and a discussant will build bridges among the different pieces and raise the conversation to a higher level of discussion of governance modes and resource reconfiguration. Two of the papers focus on the antecedents driving firms to engage in a particular governance mode (i.e., alliances, exit), whereas the other two seek to explore dynamic components in the sequential use of different modes (i.e., alliances vs. independent operations; acquisitions and divestitures). These four papers expand among different technology governance modes, theoretical lenses, and single- vs. multi-mode of governance. CEO Ideology and Investor Reactions to Alliances Presenter: Srikanth Paruchuri; Pennsylvania State U. Presenter: Razvan Lungeanu; Northeastern U. Alliance Performance and Subsequent Make-or-Ally Choices. Evidence from the Aircraft Manufacturing Presenter: Charlotte Ren; Fox School of Business, Temple U. Presenter: Louis Mulotte; Tilburg U. Presenter: Pierre Dussauge; HEC Paris Presenter: Jaideep Anand; Ohio State U. The Influence of Organizational Investors on Unrelated Businesses’ Exits Presenter: Xavier Castaner; U. of Lausanne Presenter: Nikolaos Kavadis; U. Carlos III de Madrid Exploring the Inter-Related Use of Acquisitions & Divestitures in Reconfiguration Strategy Presenter: Elena Vidal; City U. of New York, Baruch College Presenter: William G. Mitchell; U. of Toronto
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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