Study on Consumers’ Behavior on Buffen (Buffalo meat): Marketing Perspective
Bibliographic record
Abstract
The study was undertaken to examine the socioeconomic profile of buffalo farmers and to assess the marketing and consumers preference on buffen (buffalo meat) in the selected areas. Twelve districts namely: Mymensingh, Jamalpur, Moulovibazar, Bhola, Bagerhat, Feni, Potuakhali, Noakhali, Laxmipur, Chittagong, Tangail and Sirajgong were selected purposively. A total of 1400 buffalo farmers were interviewed following simple random sampling technique. Data were collected during June 2011 to April 2016 and analyzed data using SPSS software. Study revealed that the highest per cent of farmers were in age group 31-45 years indicating that farmers were mature enough to give more labour to their farming activities. On average, 88 per cent buffalo farmers were engaged purely in agriculture followed by business and service as primary occupation. The highest numbers of farmers were illiterate followed by primary education, SSC, HSC and Degree. About 49 per cent buffalo farmers had above 15 years of farming experience of rearing buffalo. Average farm size was estimated 0.95 hectare indicating small and medium category farm and average family size was calculated 6 persons per family which is higher than national average 4.9. Dependency ratio was also estimated to 0.94. The study showed that buffen contributes 7.16 per cent of total red meat production and 6.19 per cent of total meat production in Bangladesh and about 50 percent farmers reported that they did fattening before selling of buffalo. About 48 per cent consumers reported that they prefer buffen most among different kinds of meats. In view point of butcher, about 46 percent consumer preferred buffen than beef.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".