Organizing and Strategizing in the Face of War
Bibliographic record
Abstract
Wars is a persistent and recurring feature of human history. Despite the profound and multifaceted consequences of war for organizations and the growing call for management scholars to address key societal challenges, the management literature has historically paid limited attention to this critical phenomenon. This symposium seeks to address this significant gap by bringing together organizational and management scholars to explore the multifaceted relationship between war and organizations. Temporal Reframing: How Professionals Cope With Trauma and Loss of Agency Author: Madeleine Rauch; University of Cambridge Author: Shahzad Ansari; University of Cambridge How Israeli and Palestinian IT Professionals Work Together in the Context of Ethnonational Conflict Author: Pieter de Wit; - Author: Christopher Wickert; Vrije Universiteit Amsterdam Propagating a Permanent War Economy? U.S. FDI in Warring Host Countries Author: Li Dai; Loyola Marymount University Author: Yongsun Paik; Loyola Marymount University Firm R&D Choices and Bureaucratic Science, Technology, and Innovation Activity Amidst Military Coups Author: Natharat Mongkolsinh; - Author: Adam Koling; University of Oxford Author: Daniel Erian Armanios; University of Oxford Strategizing for Tomorrow’s Wars Today: Future-Making in the Military Author: Lorenzo Skade; European University Viadrina Frankfurt (Oder) Author: Sarah Stanske; Author: Jochen Koch;
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".