Beyond partnerships: embracing complexity to understand and improve research collaboration for global development
Bibliographic record
Abstract
While there is a burgeoning literature on the benefits of research collaboration for development, it tends to promote the idea of the “partnership” as a bounded site in which interventions to improve collaborative practice can be made. This article draws on complexity theory and systems thinking to argue that such an assumption is problematic, divorcing collaboration from wider systems of research and practice. Instead, a systemic framework for understanding and evaluating collaboration is proposed. This framework is used to reflect on a set of principles for fair and equitable research collaboration that emerged from a programme of strategic research and capacity strengthening conducted by the Rethinking Research Collaborative (RRC) for the United Kingdom (UK)’s primary research funder: UK Research and Innovation (UKRI). The article concludes that a systemic conceptualisation of collaboration is more responsive than a “partnership” approach, both to the principles of fairness and equity and also to uncertain futures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.089 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.079 |
| Scholarly communication | 0.032 | 0.065 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".