Participation of Poor Town Community as Agro-Entrepreneur Towards Urban Agriculture
Bibliographic record
Abstract
Poor town communities are being chosen as the main respondents in this study. The homelessness contributed negatives impact for certain country that having homelessness which involved in crime, racial problem and social problems. This also includes a few factors like migration, changes in a new lifestyle and many more personal reasons. Migration can be occurred due to the push and pull factor in the original location, while urban agriculture was introduced to reduce the negatives impact for this group to participate in urban agriculture for alternative income. The objectives of this study have identified the level of participation of the poor town community in urban agriculture entrepreneur. The significance of this study is to make the number of decreasing homelessness by providing a job for them and to improve the quality of idle land. 79 respondents were involved in this study. Most of the respondent was from homelessness. In this study, purposely sampling method was used to prevent any bias. There are three analysis tests run to obtain the information from raw data. The test used in this study was a descriptive test which included the mean, mode and standard deviation. Chi-square test was used to determine the relationship between the factorial. The result showed to implement the urban agriculture needs the right attitude, knowledge and perception, although the chi-square showed no significant value on socioeconomics towards urban agriculture within the poor town community.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".