Toward community food security through transdisciplinary action research
Bibliographic record
Abstract
To solve the world’s most complex problems, research is increasingly moving toward more transdisciplinary endeavors. While a lot of important work has explored the characteristics, challenges, opportunities, and operationalization of transdisciplinary research, much less is known about the circumstances that either facilitate or hinder the research process, particularly from the perspectives of graduate students who often participate in them. In this paper, we aim to address this gap by contributing our own experiences as a team of four graduate students and one community partner that collaborated on a food security project. To support our collaboration, we develop and apply an analytical framework that integrates transdisciplinarity and action research. Through principles of reflexivity, participation and partnership, methods and process, and integration, we find that the framework facilitated the development of shared purposes, mutual responsibility, and meaningful relationships, resulting in the co-creation of a guidebook for farmer-led research. Our main concern with the framework is not achieving the full integration of our disciplines and practices. Transdisciplinarity together with action research holds significant promise in a food security context, but only in the “right” circumstances, where considerable time is spent building relationships, opening communicative space, and reflecting on the work with collaborators.
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 | 0.106 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.006 | 0.045 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".