Learning from the CO‐CREATE project: A protocol for systems thinking across research (STAR)
Bibliographic record
Abstract
The CO-CREATE project aimed to work with young people to create, inform, and disseminate obesity-preventive evidence-based policies using a complex systems perspective. This paper draws lessons from this experience and proposes a protocol for embedding systems thinking within a research project. We first draw on existing systems thinking frameworks to analyze how systems thinking was translated across CO-CREATE, including the flow and relationship between the work packages and in the methods used. We then take the lessons from CO-CREATE and the principles of existing systems thinking frameworks-which focus on various points of intervention planning and delivery but not on research projects as a whole-to formulate a protocol for embedding systems thinking across a research project. Key lessons for future planning and delivery of systems-oriented research projects include incorporating "boundary critique" by capturing key stakeholder (adolescent) values and concerns; working to avoid social exclusion; ensuring methodological pluralism to allow for reflection and responsiveness (with methods ranging from group model building, Photovoice, and small group engagement); getting policy recipients to shape key questions by understanding their views on the critical drivers of obesity early on in the project; and providing opportunity for intraproject reflection along the way.
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 | 0.330 | 0.328 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
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".