Portrait of a Teacher of Educators Prof. Dr. Orhan Okay as a Role Model
Bibliographic record
Abstract
In education, the importance of effective and behavioral transfer besides cognitive information is a fact. In this sense, an educator is expected to be an example in terms of his behaviors as well as transferring his knowledge to the students. The best way to raise good students and form the desired behavior in them is the educators who personally touch the lives of their students being role models. In the Turkish academic world, M. Orhan Okay has a remarkable place not only with his scientific studies but also with his “human work” upbringing style. This study is carried out with document analysis which is one of the qualitative research methods. In this study, it is underlined that Okay is a role model/an ideal educator, further every aspect of his ideal educationalism, which complements his exemplary scientist identity, is emphasized. In this respect, besides being a well-equipped scientist, he showed his students and the whole academic environment how an example of an ideal / role model should be. In either way, he expected his students to do what he did personally (as an exemplary person in the academic and non-academic world). In conclusion, it is hoped that the educator deformation which has increased in recent years will disappear to a certain extent thanks to exemplary educators like Professor Okay.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".