Bibliographic record
Abstract
Government Procurement Act came into force as of the date of its promulgation in 1988, and according to Article 98 of the act, the way of the motivation of hiring the disabled and the indigenous with obligation for a winning tenderer is a minimum of two percent of the total number of employees. After that, the Indigenous Peoples Employment Rights Protection Act was proclaimed in 2001, and the protection of indigenous percentage employment in government procurement system that the indigenous employees should comprise at least 1% of the total employees is prescribed by Article 12 of the indigenous people employment rights protect act. Since the implementation of Article 88 of the government procurement act in 1998 and Article 12 of the indigenous peoples employment rights protection act in 2001, it has encountered problems and issues of law and to deal with disagreement and resistance because there have been too many levels, units, and organizations involved in the process of the implementation. The first purpose of this research focuses on the changes of the indigenous employment protection systems in Taiwan. Second, I probe into the contents, the process of implementation, and the consequences of the indigenous percentage employment in the two laws from 2001 to 2012 by the data I collected. Third, I analyze the issues and the precedents of administrative appeals and lawsuits of indigenous percentage employment in public and private sectors. Fourth, I gather information about the systems of indigenous protection employment from the United States, Canada, New Zealand, and Australia. Finally, I propose recommendations about law amending of the indigenous percentage employment in the two laws and the indigenous employment policies in the future. According to the analysis and results in my research, the concerns of the attributes of indigenous people, and the social changes nowadays, the research recommendations are as follows: 1.The government should amend laws in the direction of increasing the numbers of long-term and stable jobs for indigenous people. 2.The government should establish a comprehensive system of employment matching and human resources survey. 3.The government should devote itself to cultivating every aspect of talent in the labor market and encourage indigenous people to enroll and to engage in advanced studies. 4.To integrate tribes’ resources and to promote the employment status of indigenous peoples. 5. To shape the culture of entrepreneurship of indigenous youth.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".