Industrial Robotic Systems & International Human Rights
Bibliographic record
Abstract
The development and growth of industrial robots started in 1947. The velocity of this process has increased as a result of development technology. Now, industrial robots have broad applications. They can be substituted for human force in different industries. The ever increasing growth and development of robotic technology in the field of industry was always challenging. One of these important challenges emphasizes on the negative effect of robotics on employment rate. As a result of cost reduction and production improvement, industrial countries have been motivated to employ robots and substitute them for workers in production lines. However, the broad use of robotic systems in the field of industry can have negative consequences in different societies. One of the common and negative effects of these systems is the reduction of employment opportunities which increases unemployment for those who look for jobs and for employed individuals. It can lead to employment insecurity and threat the health and safety of workers. These matters violate the human rights regarding the security and health of individuals, equality of opportunity, and particularly the employment rate. It also violates the employment standards supported by the international human rights instruments.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.184 | 0.081 |
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".