Human Rights and Minority Languages: Immigrants’ Perspectives in Greece
Bibliographic record
Abstract
Human rights and their fortification through conventions and treaties are thought to be one of the greatest achievements of the previous century. A very important category of human rights is the Linguistic Human Rights (LHR). Linguistic Human Rights are connected to basic human rights and are of great importance in policy and planning. There have been numerous researches on language policies and in educational systems around the world. However, minority populations’ opinion, for example refugees’ opinion, is rarely represented in these researches. The present research aims at exploring the existing language policies in Greece in reference to minority languages. For the needs of this research six adult refugees participated in short semi-structured interviews. Even though participants seemed to be unaware of the term “Linguistic Human Rights”, most of them referred to the difficulty they have in exercising major human rights due to the monolingual policies that are followed in Greece. Taking into consideration the importance of Linguistic Human Rights and people’s need to use their mother language(s) in Greece, the last part of this research includes suggestions and ideas towards multilingual practices that come from different countries around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".