Policy in the Peaks: Cybercartography and Traditional Ecological Practices to Diversify Pasture Policy-Making in Naryn Province, Kyrgyzstan
Bibliographic record
Abstract
Kyrgyzstan's pasture management policies have been challenged by the limited capacity of its nascent, village-level committees and pasture user groups.The collapse of supporting Sovietera institutions that collected up-to-date information means policies have little connection with actual practice on the ground.As a result, rural Kyrgyz livelihoods have stagnated in Naryn province.A cybercartographic approach with user-generated data is implemented to visualize traditional practices on an online atlas.Participants identify pasture management, ecological monitoring, and medicinal plants as key categories of practices to be mapped.Both the produced atlas and the process of making the atlas are examined for their impact on pasture stakeholders' roles in pasture management.Spatial and interview results show spatially different representations of pastures by various groups and a dialogue-building effect of visualizing practices on an atlas.Demonstrating spatial and thematic linkages between groups offers new partnerships and deeper possible engagement of pasture users in managing pastures.These results are discussed in the context of informing a future pasture governance tool.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".