Cost-Benefit Analysis of Herders’ Household Business Scale in the Multi Household Grassland Management Patterns: A Case Study of Maqu County in Qinghai-Tibetan Plateau
Bibliographic record
Abstract
This study conducted an analysis of total production costs, gross production values, and net margins across varying scales (small, medium, and large) within herder households operating under the multi-household management pattern. Data was sourced from a random sample of 35 herder households representing six multi-household management patterns in Maqu County, Qinghai-Tibet Plateau. The results revealed that average total production costs per sheep unit were $168.43, $107.36, and $92.89 for small, medium, and large-scale operations, respectively. Gross production values in these scales were $243.50/SSU, $245.23/SSU, and $239.53/SSU. Significantly, large and medium-scale herder households achieved higher net margins, at $146.64/SSU and $137.87/SSU, while small-scale households obtained $75.06/SSU. An intriguing revelation is that net margins for large and medium-scale households predominantly fall within the range of $100.01/SSU to $200.00/SSU, signifying that while scaling may curtail total production costs per sheep unit, it does not assure enduring increases in net margins. These findings hold paramount implications for policymakers as they reassess the feasibility of upscaling multi-household management pattern operations for grassland ecological restoration on the Qinghai-Tibet Plateau. While scaling up can yield cost efficiencies, it does not inherently translate into sustained net profit growth. Hence, astute consideration of these insights is imperative in evaluating the potential of scaling up multi-household management patterns for grassland ecological restoration initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".