Living knowledge: persistence and adaptation of traditional ecological knowledge in East Ujimchin, Inner Mongolia, China
Bibliographic record
Abstract
In the face of considerable socioeconomic and environmental challenges, traditional ecological knowledge (TEK) systems around the world continue to be practiced and maintained. This study examines how three traditional pastoral practices, mobile grazing, herd breeding, and herd sharing, persist and change in East Ujimchin Banner, Inner Mongolia, China. Based on nine months of fieldwork, including household surveys, interviews, and participatory mapping workshops, we analyze how these practices have evolved under environmental and socioeconomic pressures. Mapping data from 30 households shows that all continue some form of seasonal or spatial mobility, though the frequency and range of movement have significantly declined because of restricted land access. Survey results from 227 herders show that over 80% recognize 26 out of 31 traditional breeding traits, and more than half still actively use 19 of these traits. Beyond their persistence, these practices are multifunctional, fulfilling diverse ecological, economic, and cultural roles. Our results show that herd sharing is used not only to support families in need, but also to address land shortages, labor constraints, and adaptation to severe climatic events, while simultaneously contributing to the preservation of cultural traditions. Additionally, our results show that these practices serve overlapping functions, particularly regarding climate adaptation. During extreme weather events, mobile grazing enables herders to access more favorable pastures; selective herd breeding ensures livestock are better able to withstand weather conditions; and herd sharing allows herders to redistribute livestock to mitigate potential losses. Together, these practices demonstrate the dynamic adaptability and continuous relevance of TEK in a rapidly changing context.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".