Canada's Contribution to Neglected Tropical Disease Research: A Co-authorship Network Analysis
Bibliographic record
Abstract
Objective: NTDs are a group of thirteen communicable diseases that thrive in impoverished settings and are a prime example of a global health issue in need of collaborative research solutions. The aim of this study was to design and apply an accessible yet systematic approach for analyzing the publication trends and collaboration structure of Canada’s neglected tropical disease (NTD) research network. Co-authorship network analysis is an emerging technique for understanding the complex dimensions and results of collaborative research. Methods: Multiple methods and measures were used to support this study including: an academic and grey literature review; a systematic bibliometric procedure (publication activity, quality, specializations and collaboration rates) and a co-authorship network analysis (network size, components, density, cliques, clustering, centralization and core-periphery) of countries and institutions contributing to Canada’s NTD research. Results: Over the past sixty years (1950-2010) there has been a notable increase in Canada’s NTD publication activity. Canada’s NTD researchers specialize in Leishmania and African sleeping sickness research and are mainly affiliated with academic institutions within Canada (McGill University, Laval University and University of British Columbia). International co-authorship activity is largely with OECD countries (United States, United Kingdom and France) and some non-OECD countries (Brazil, Iran, and Peru). The core of Canada’s NTD research network includes a tightly connected group of OECD countries and two African countries (Uganda and Kenya). The countries on the periphery of the network are predominantly non-OECD countries which fall within the networks lowest GDP percentile. Canada’s Leishmania research network is regionally clustered with central ‘bridging’ and ‘bonding’ institutions. This contrasts to the more permeable structure of Canada’s African sleeping sickness institution network. Conclusion: The methods and findings from this study implicate Canada’s global health research and institutional policies. The findings suggest that if North-South research collaborations are a strategic global health funding priority, then methods and mechanisms to map existing networks and leverage the work of leading and emerging researchers and institutions are needed. Support to reform Canadian institutional policies related to inventions, patenting and technology transfer is required to transform existing insular practices and incentivize global health innovation and research partnerships.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.058 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".