Avian Influenza and Employment Decisions of Poultry Farmers in the Federal Capital Territory of Nigeria
Bibliographic record
Abstract
The outbreak of Avian Influenza in Nigeria has led to job losses, health problems, reduction in expected income of poultryfarmers and a decrease in the demand for poultry products. This study was designed to determine the monetary value ofstock lost, identify the determinants of the future employment decisions and the constraints faced by poultry farmers in theFederal Capital Territory of Nigeria. Data for the study was collected from 40 poultry farmers who have suffered lossesdue to the outbreak of the disease. The total monetary value of the stock lost in the study area as at 2007 was N142, 741,000. 45% of the respondents have abandoned poultry production while 32.5% have reduced the size of their poultrybusiness. Furthermore, only 22.5% have restarted their poultry business without reducing the quantity of the initial stockbefore the outbreak of the disease. The determinants of the decision to abandon were; amount of compensation received,educational level of the poultry farmer and total number of stock lost. The factors influencing the decision to reduce thescale of operation were; level of education of the farmer, years of experience in poultry production and the amount ofcompensation received from government. The post Avian Influenza outbreak constraints faced by the farmers were;inadequate compensation, low patronage by customers and low level of accessibility to agricultural credit institutions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".