Globally important agricultural heritage systems (giahs) of china: the challenge of complexity in research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The challenge of researching Globally Important Agricultural Heritage Systems (GIAHS) as complex systems forms the subject matter of this study. Complex adaptive systems are those that combine natural ecological processes with human interactions to produce a mutually supportive agro‐ecological system. In China, these highly varied systems have the added dimension of long historical time, in that they have evolved over many centuries and thus add a historical dimension to the natural and human dimensions of complexity. In preparing research on GIAHS, it is clear that seeing GIAHS sites as whole systems is an essential starting and ending point. Examining the adaptive capacity of a GIAHS with its multiple scales and complex interdependencies is a major challenge for researchers accustomed to specialized disciplinary thinking. A GIAHS represents a mature agro‐ecological system with human agency as a central component that has been honed over many centuries, and has already adapted to many perturbations and changes. The beauty of the GIAHS is in the integration of custom, knowledge, and practice, and it should be studied for its “wholeness” as well as for its resilience and capacity for “self organization.” The agro‐ecological approach opens the possibility of researching a system as a whole and of taking its complexity seriously. This study reviews the essential features of the GIAHS as a complex adaptive system where uncertainty is normal and surprise is welcome and, in a case study of Qingtian rice–fish culture system, focuses on new perturbations, namely loss of young people and the introduction of tourism.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it