The quality of milk: adoption of Canadian Dairy Technology in a milking station in rural Inner Mongolia
Bibliographic record
Abstract
This thesis investigates the introduction of Canadian Dairy Technology to small dairy farm households in Horinger County, Inner Mongolia. The research describes the present Chinese dairy industry and the household level dairy operations in rural Horinger County. Analysis of the households to the adoption of Canadian Dairy technology is explored within a social and resource context. Participatory methods were utilized to reflect the importance of social perspective towards the technological transfer. The study revealed households quality of life has improved since the ownership of cows and milk sales are the principle sole income. All households desired dairy expansion and milking equipment ownership. Construction of tie stalls providing a "clean and dry" cow environment provided six demonstration sites in sand and rubber mat bedding surfaces. The households' lack of understanding in dairy and milk quality knowledge was a prominent barrier and adoption constraints were lack of policy, power, mechanization, land and capital. Milk quality was not acceptable and is a public health risk. Zoonosis will become a public health issue unless education and government programs recognize the potential risks. The larger industrialized farms with higher milk quality and traceability will eventually put pressure on the small household system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".