Bibliographic record
Abstract
Stimulating the transformation towards more sustainable, healthy, and just food systems is an urgent challenge. However, this is also a highly difficult challenge, since food systems are complex systems that are resistant to change. The type of research that explicitly aims to facilitate systemic change, called transformative research, has the potential to accelerate sustainable food system transformation. Such research is characterised by knowledge co-creation, experimentation, and learning and reflexivity among a variety of different actors. However, to date, transformative research is scarce in practice. With this thesis we aim to contribute to an increased insight into how we can better understand and enhance the transformative capacity of research, so that it can act as a catalyst for sustainable food system transformation. To this end, we performed six studies that followed an emergent design and were conducted in the context of the European Union project FIT4FOOD2030 (2017–2020). With the first two studies, we aim to increase our understanding of research as a catalyst for food system transformation. We argue that in order to scale up transformative research, we need to transform the systems in which knowledge is produced and used, which we refer to as Research & Innovation (R&I) systems. Just as food systems, R&I systems can be best viewed as complex systems that are resistant to change. We introduce the FIT4FOOD2030 project as an example of a multi-level boundary innovation that aims to trigger the transformation of food systems and their coupled R&I systems. In the last four studies of this thesis, we zoom in on two capacity building strategies that we consider key for supporting such a coupled systems transformation, namely: competence development and network building. Our findings show that transformative research approaches require different competences among a variety of actors, transformative researchers in particular, which, we argue, need to be seen in relation to envisioned actor roles. Examples of important actor roles are that of change agent, knowledge broker, and reflexive facilitator. Higher education institutes have an essential role to play in stimulating such competence development among (future) professionals by institutionalising transformative research approaches. This requires the development of novel educational programmes that more strongly connect to regional food networks, such as Food Policy Networks (FPNs), development of support mechanisms for teachers and researchers, and a structural integration of training and learning activities within transformative projects such as FIT4FOOD2030. R&I policy innovation is essential to create an enabling environment for transformative research approaches, such as Real-world Labs, and to stimulate portfolios of transformative projects so that projects can effectively build upon each other over longer periods of time. Based on our findings, we propose future research into 1) effective (combinations of) strategies for competence development among (future) professionals, referring to research into the institutionalisation of transformative research approaches in higher education institutes as well as research into the dynamics of translocal learning processes among professionals engaged in transformative projects, 2) effective strategies for competence development among children, 3) the functioning of and dynamics within transformative research formats, such as Real-world Labs, 4) the current state of food systems research, referring to the need for quantitative studies to investigate whether, and to what extent, a trend is visible towards transformative research for food system transformation, and finally, 5) the institutionalisation of transformative innovations as well as the deliberate destabilisation of unsustainable cultures structures and practices in both food systems and R&I systems.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.043 |
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".