Bibliographic record
Abstract
In many countries around the world, determining what languages or dialects should be the instrument of education in schools is a major issue. Today, in Kurdistan this issue is a growing concern, particularly amongst students who speak a Kurdish dialect known as Kurmanji (Hassanpour, 2008, 2012). This thesis is an attempt to investigate the difficulties that Kurmanji students encounter in schools. Its aim is to investigate their attitudes and perceptions towards the Sorani dialect and Sorani speakers. For this reason, twenty students, including 10 males and 10 females, participated in this study. The students were from a Kurmanji town known as Akre. For this study,the qualitative method was used as it allowed the students to describe in detail the difficulties they have faced in schools and the attitudes they had towards the Sorani dialect and its speakers. The research revealed that Kurmanji students faced different kinds of problems not only in their classes, but also in their neighborhoods and communities. These problems ranged from simple issues with classroom curricula and pedagogical approaches, to more extreme problems. Many of the extreme problems involve being humiliated in the classroom by their teachers, being unfairly disciplined and penalized for language mistakes, and many other forms of punitive action. Further, the study reported how these issues have been addressed through the use of a method called bidalectialsim in several countries, such as America, Australia, Canada, England, etc. This method teaches the dominant dialect through the use of the students own native dialect (Yiakoumetti, 2006, 2007). These approaches and solutions can offer Kurdistan a blueprint for how to address their own problems and how to pave the way for speakers of Kurmanji to learn the targeted standard dialect of Sorani in Kurdistan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".