Factors Influencing Unsafe Behavior in Somber Small Industry Centers (SIKS) Using Logistic Regression Analysis
Bibliographic record
Abstract
Tofu is a characteristic of Indonesian food, so it is not uncommon if this tofu food is found in Balikpapan City. Tofu is commonly produced in small and medium scale industries that are commonly found in the city of Balikpapan. The technology used in the tofu production process is still very simple, still uses human labor, and the process is less than optimal. The tofu production process begins with the washing, grinding, and pressing processes carried out by humans. This study aims to analyze the influence of the work environment and worker identity on Behavior Based Safety in the SIS tofu making industry. The population in this study is workers in SIKS somber totaling 32 workers from 14 home tofu production sites as for how to collect samples by direct observation and interviews with workers (response variables), namely unsafe behavior in the form of not wearing proper clothes while working (Y), and independent variables (predictors), namely a safe workplace environment (x1), Air Ventilation Conditions (X2) , Layout Conditions (X3), Working Duration (X4) Denoted 1 if > have worked for 3 months and 0 if < 3 months, worker status (X5), use of PPE in the form of boots (x6), educational status (x7) This study uses binary logistic regression analysis ?(x), where the workplace environment and workplace layout have the opportunity to have a significant influence on the unsafe behavior of workers (not wearing proper clothing) production workers know SIKS Somber.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".