Working with the “Enemy”: Supervised Space, Free Space, and Cross-Border Collaboration amid Geopolitical Rivalry
Bibliographic record
Abstract
As the world grapples with intensifying geopolitical competition and ideological conflict, many organizations face the daunting task of navigating the complexities of geopolitics and fostering effective cross-border partnerships. For members of these organizations, such political dynamics might create new barriers to their ability to carry out collaborative activities. In a historical case study of the Apollo–Soyuz Test Project—an unprecedented partnership between the space programs of the United States and the Soviet Union at the height of the Cold War—we identify how organizational members navigated the turbulent geopolitical environment. We found that collaborative meetings between organizational members were limited to a supervised space that ensured government oversight but created interactional barriers. Organizational members realized that their ability to overcome these challenges would require them to develop practices outside of the organization, using boundary work to carve out free space outside the purview of political supervision. The free space served as a laboratory in which they reconciled informational, techno-cultural, and ideological differences and created solutions to the challenges they faced in the supervised space through translation work. Our study theorizes how geopolitics complicates the interactional processes of cross-border partnerships and underscores the importance of free space for fostering collaboration amid geopolitical rivalry. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15574 .
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".