Teamwork in Action: Collaborating Across Borders
Bibliographic record
Abstract
Abstract Teamwork in action: Collaborating across borders Academicians are often found working in silos, focused on producing scholarship to attain tenure. The initial years of an academician are intense and requires them to focus on output rather than forging collaborations. Essentially they may not have the time to pursue collaborations. Collaboration between faculty has the potential to increase their productivity and help achieve tenure and promotion quicker. In this paper, the authors discuss both challenges and opportunities that faculty face when they collaborate. The paper describes a case study of a team faculty including their efforts in creating an effective team and their guidelines to successfully work collaboratively over a 4 year period while addressing challenges that arose. The team comprises of three members coming from different engineering fields and different cultural backgrounds and working at different universities across borders. The paper discusses how the team were able to cross the different borders set in their way: cultural background, discipline, conflicts and physical borders by sharing their best practices for effective collaboration, the guidelines set for this collaboration to make it successful, the similarities and differences among the team members that drove to a stronger and more productive team. The team will also discuss their experiences that brought them together from across international borders, uniquely different engineering fields and the variety of experiences in conflicts. This paper uses the case study to address the research question of how prior experiences can affect team success and the likelihood of team success Furthermore, this study will shed light on important factors that could be transferable to other teams leading to more effective 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 | 0.079 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".