Do research collaborations age like wine? Absolute and relative measures of CANZUK research partnerships’ strength since the 1950s
Bibliographic record
Abstract
Although previous studies of today's globalised and competitive research landscape have mentioned the research collaborations of CANZUK countries (i.e., Australia, Canada, New Zealand, and the United Kingdom), none have yet studied them in detail. Further, such studies have used different measures of international research collaboration (IRC), resulting in disparate findings. This paper, therefore, analyses the strengths of CANZUK research collaborations, how those collaborations have changed over time, and assesses the effect of three ways of measures on the results (absolute strength, bilateral similarity, and multilateral similarity). We provide a detailed characterisation of the CANZUK research network and its relationships with partner countries, which reveals that the most collaborative CANZUK countries are the UK and Australia, among other findings. We also confirm that many findings differ depending on which measures are used. We offer an explanation of this difference with reference to the nature of the measures (i.e., what they really measure) and make suggestions for suitable measures in future studies depending on their purpose. Finally, we discuss how this study's findings can be used by research policy makers (in CANZUK and elsewhere) in deciding on research strategy and by researchers in appropriately measuring IRC.
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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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".