A relational conceptual framework for multidisciplinary health research centre infrastructure
Bibliographic record
Abstract
Although multidisciplinary and team-based approaches are increasingly acknowledged as necessary to address some of the most pressing contemporary health challenges, many researchers struggle with a lack of infrastructure to facilitate and formalise the requisite collaborations. Specialised research centres have emerged as an important organisational solution, yet centre productivity and sustainability are frequently dictated by the availability and security of infrastructure funds.Despite being widely cited as a core component of research capacity building, infrastructure as a discrete concept has been rather analytically neglected, often treated as an implicit feature of research environments with little specification or relegated to a narrow category of physical or administrative inputs. The terms research infrastructure, capacity, and culture, among others, are deployed in overlapping and inconsistent ways, further obfuscating the crucial functions of infrastructure specifically and its relationships with associated concepts.The case is made for an expanded conceptualisation of research infrastructure, one that moves beyond conventional 'hardware' notions. Drawing on a case analysis of NEXUS, a multidisciplinary health research centre based at the University of British Columbia, Canada, a conceptual framework is proposed that integrates the tangible and intangible structures that interactively underlie research centre functioning.A relational approach holds potential to allow for more comprehensive accounting of the returns on infrastructure investment. For those developing new research centres or seeking to reinvigorate existing ones, this framework may be a useful guide for both centre design and evaluation.
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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 | 0.035 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".