Bibliographic record
Abstract
Development of human rights and citizenship rights is based on publication of its concepts among the people in global society. This publication of concepts and introduction of the people from different nations to human rights are done through education. Media with their role in transfer of information and knowledge have educational function. Media with their educational function ensured education right as one of the human rights while informing the public thoughts with their own rights and increasing their demands through human rights and citizenship rights education. Human rights and citizenship rights education activate the people of society in normalization of the related rules and this media education which is directly and indirectly related to obligation of states to right of education binds the states to respond to increased demands of human rights and citizenship rights and take action regarding development of the human and citizenship rights in national and international level. Human rights and citizenship rights have exclusive capability which leads to increased awareness of states with human and citizenship rights and increased demands of states and international society considering high number of media addresses and diversity of their content in presentation of materials about human and citizenship rights in education for all society levels and increased demand leads states and international society to develop norms of the human and citizenship rights through legislation, codification and enactment of the conventions on human rights and this process leads to development of human and citizenship rights at local and global levels.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".