The notion of Charter Schools and Its Feasibility in Turkey
Bibliographic record
Abstract
The purpose of this study was to investigate the feasibility of Charter School system in Turkey, which was opened firstly in State of Minnesota of United States and was expanded to approximately 40 states in America today and also, in practice in some countries such as Canada, New Zealand, United Kingdom, Sweden and Norway.Charter Schools are educational institutes that can be opened by signing a contract between a country's institution responsible for education and a person or a group who wants to be responsible for the management of this school.This system was based on performance and accountability and pursued more competitive and innovative goals.Moreover, Charter Schools put emphasis on democracy and equality in education by being free, addressing to all students living in the region where school was located and considering individual differences and diversity on behalf of students.Eight volunteering faculty members were chosen by criterion sampling who were working in the field of Educational Sciences of universities in Turkey.Interviews were conducted with participants who were informed about the structure and operation of this system in advance.The results of the study suggested that Charter Schools were advantageous in terms of individualism, diversity and flexible curriculum though flexible curriculum, monitoring and audition process could lead to some problems when it was practiced in Turkey.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".