Bibliographic record
Abstract
In the 21st century, science and technology have led to so many negative situations as well as positive developments for humanity. These negativities also affect human beings in a very intense way and in a natural result of this, people affect other’s livings negatively. The general belief that the decelerating the events, actions and moral corruptions which lead to missing the peace of communities and the abolition of this negative situation will be ensured only by the development of the values which the individual possesses. Also states are taking precautions and making plans for this issue. So, value education in many countries has recorded a rapid acceleration in recent years. In Turkey, there are also some sections which have theoretical course curriculum and some scientific activities about values. However, it is considered that development in value and value education concepts are not enough and the applied courses such as physical education and sports, which aim to improve the individual as a whole, do not take place as much as within the scope of value education. For this reason, it is aimed to establish a point of view in the light of the works done in the Turkish science literature and the activities carried out by the related stakeholders for value education. Research was prepared as a document analysis form. Resources related to the physical education and value education in Turkey were collected and general evaluations were made through the obtained data and finally some suggestions were made.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".