Bibliographic record
Abstract
The aim of this research is to reveal the thoughts on the history program of the teacher candidates who have recently graduated from the history teaching undergraduate program. In this research, a case study model from qualitative research methods was adopted. The study group of this research consisted of 49 teacher candidates, 18 female and 31 male. The teacher candidates were senior students to graduate at the end of the 2019-2020 academic year, and they were selected from education faculties at three state universities. Within the framework of this study, an easily attainable method was adopted in the selection of universities and faculties, and the criterion sampling, a purposeful sampling method, was used while choosing the study group. The data of the study were collected via e-mail correspondence with a questionnaire consisting of open-ended questions created by the researcher. The data obtained from the study group were analyzed using descriptive analysis. When the results of the study are evaluated in general, it could be stated that the history teacher candidates in the study group mostly have positive opinions about their undergraduate education. However, teacher candidates expressed that the course contents were very theoretical and added that the number of practical courses should be increased.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".