Non-Disparities Principles to Establish a Sustainable Garment Industry for Indonesian Female Workers
Bibliographic record
Abstract
The garment industry is a top-five contributor to Indonesia’s that associated their counterparts in the metal and chemical sectors and contribute to Indonesia’s economic growth in recent years. This industry is also an essential source of employment, accounting for about a quarter of all manufacturing jobs. The textile industry faces the challenge of competing with other textile-producing countries, with labour costs affecting competitiveness. Another challenge is fulfilling international labour standards regarding the treatment of workers including working conditions in garment factories, especially in MSMEs scale. Indonesia has ratified the Convention on the Elimination of All Forms of Discrimination Against Women and eight core of International Labour Organization Conventions. The main issues are occupational health and safety, the rights of women to be free of sexual harassment in the workplace, working hours and conditions, freedom of association, and the right to collective bargaining, because ensuring non-disparities principle is about integrating moral and social imperative above economic gain so it aligns with ILO framework and Indonesia’s constitution.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".