Food futuring in Timor-Leste: Recombinance, responsiveness, and relationality
Bibliographic record
Abstract
The pluralistic nature of food culture and food systems produces complex and blended realities for research, often prompting approaches that embrace mixed methods and cross-sector partnerships. In parallel, calls for the decolonization of research methods have brought attention to the importance of relationality when working with local communities and traditional knowledge holders. This article presents the process and outcomes of the Timor-Leste Food Innovators Exchange (TLFIX), a multifaceted initiative centered on the contemporary and historic foodways of Timor-Leste, including current challenges to individual health, cultural identity, and economic-ecological sustainability brought about by centuries of colonial and transnational influence. Conceived within an international development context, TLFIX aimed at building local empowerment, economic development, and social change. Methods included quantitative, qualitative, and material-based approaches, including surveys, storytelling, and culinary innovation. As a “consulting academic” on the project, I contributed to the research design, coached team members on storytelling-as-method, and participated in a portion of the work. For the current text, I use the notions of recombinance, responsiveness, and relationality to interpret our collective experience and to frame an example of carrying out mixed-method and mixed-participant work in complex food contexts. As a whole, this example illustrates ways in which to leave space for improvisation and emergence within food practice and scholarship.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".