Studying Practices and Processes to Explore Cross-Boundary Collaboration
Bibliographic record
Abstract
Cross-boundary collaborations are necessary conditions when engaging with open science, innovation, and strategy to also tackle complex societal problems. While cross-boundary collaborations bring together a richness of benefits, they are also challenging due to heterogeneous and sometimes conflicting practices, backgrounds, or (temporal) structures, among others. To complicate matters, actors often cross multiple boundaries (e.g., community, cultural, disciplinary, gender, geographical, institutional, industry, knowledge, and organizational), and new boundaries may surface and disappear as collaborations evolve over time. Cross-boundary collaborations, with new forms and types emerging, challenge managers, employees, and workers. In this symposium, we set out to ask panelists about why and how a practice and process lens help to investigate challenges and opportunities related to “conventional” and new forms of cross-boundary collaboration.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".