Bibliographic record
Abstract
According to the 2012 Council of Agriculture, Executive Yuan data pointed out that Taiwan's beef consumption per person per year for about 3 to 4 kg, imports growth from 65.4 thousand tonne in 2001 to 109.6 thousand tonnes in 2010, more than 90 percent of all imports beef, mainly from the USA, Canada, New Zealand and Australia and other countries. American beef imports in recent years, there is mad cow disease concerns and clenbuterol residue, resulting in consumers' meat safety panic. Was explored and the literature that discuss pay more for meat nutrition awareness, safety perception and taste the flavor and other issues lacking In view of this, the need for people to discuss the buying behavior of American beef. The study conducted a questionnaire survey using convenience sampling, and consumers to buy American beef and related products, in 2010-2011, a total payment of 600 questionnaires, the effective sample size of 530, with 88% efficiency. The data were analyzed by descriptive statistics, Pearson (Pearson) correlation analysis and stepwise multiple regression analysis for hypothesis testing. The results showed that:To buy American beef consumers, male, age 30 to 39 years to a maximum of, From the educational level generally in the majority of college degree, Annual household income ranged between 1 million to 2.296 million dollars. Nutrition awareness, safety perception, taste and flavor, associate positively with purchase intentions. Nutrition cognition have a significant effect on purchase intention; Perception of safety have a significant effect on purchase intention; Taste flavors have a significant effect on purchase intention.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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; both teacher heads agree on what is shown here.
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".