Establishing and maintaining international collaborative research teams: an autobiographical insight
Bibliographic record
Abstract
Despite the growing impetus for international collaborative research teams (ICRT), there are relatively few resources available to guide and support researchers through the processes of establishing and maintaining ICRTs. In particular, no articles were found that provided researchers’ firsthand accounts of being a member of such a team. Having access to such personal accounts can help both experienced and novice researchers learn more directly about what to expect, as well as the benefits, challenges, pitfalls, and success strategies for establishing and maintaining ICRTs. The authors used phenomenological autobiographical reflective journaling to capture their experiences as members of ICRTs. In this article we provide an overview of key themes that emerged from the analysis of our reflections as members of ICRTs. These themes include: benefits, challenges, and strategies for success. Our aim is to share our first-hand experiences of what it is like to establish and participate in ICRT. It is not our intention to provide readers with prescriptive guidelines on how to set up and maintain ICRTs. Every ICRT is unique and some of these ideas may or may not apply in every case. Instead, we are describing what worked for us, hoping that others may benefit from our experience. Consequently, we suggest that the focus of ICRT should be on the benefits thereof which promote and encourage interaction between disciplines, transfer of knowledge and techniques and personal and professional development.Keywords: international, collaborative, research, teams, interdisciplinary
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How this classification was reachedexpand
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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".